subject:(Particle Swarm Optimization)

University of Georgia

1. Brooks, Philip. Particle Swarm Optimization and priority representation.

Degree: MS, Artificial Intelligence, 2010, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/brooks_philip_201012_ms

► In this thesis I examine the poor performance of Discrete Particle Swarm Optimization when applied to forest planning, an optimization problem in which the goal… (more)

Subjects/Keywords: Particle swarm optimization

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APA (6^th Edition):

Brooks, P. (2010). Particle Swarm Optimization and priority representation. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/brooks_philip_201012_ms

Chicago Manual of Style (16^th Edition):

Brooks, Philip. “Particle Swarm Optimization and priority representation.” 2010. Masters Thesis, University of Georgia. Accessed February 19, 2020. http://purl.galileo.usg.edu/uga_etd/brooks_philip_201012_ms.

MLA Handbook (7^th Edition):

Brooks, Philip. “Particle Swarm Optimization and priority representation.” 2010. Web. 19 Feb 2020.

Vancouver:

Brooks P. Particle Swarm Optimization and priority representation. [Internet] [Masters thesis]. University of Georgia; 2010. [cited 2020 Feb 19]. Available from: http://purl.galileo.usg.edu/uga_etd/brooks_philip_201012_ms.

Council of Science Editors:

Brooks P. Particle Swarm Optimization and priority representation. [Masters Thesis]. University of Georgia; 2010. Available from: http://purl.galileo.usg.edu/uga_etd/brooks_philip_201012_ms

2. YANG, Shiqin. Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究.

Degree: 博士(理学), 2017, Hosei University / 法政大学

URL: http://hdl.handle.net/10114/13303

► The premature convergence problem and the exploration-exploitation trade-oﬀ problem are the two major problems encountered by many swarm intelligence algorithms in both global optimization and… (more)

▼ The premature convergence problem and the exploration-exploitation trade-oﬀ problem are the two major problems encountered by many swarm intelligence algorithms in both global optimization and large scale global optimization. This thesis proposes that the two main problems could be handled by several variants of Particle Swarm Optimization (PSO) developed below. Five variants of homogeneous PSO have been developed for multimodal and large scale global optimization problems, and two variants of dynamic heterogeneous PSO for complex real-world problems. First of all, an individual competition strategy is proposed for the new variant of PSO, namely Fitness Predator Optimization (FPO), for multimodal problems. The development of individual competition plays an important role for the diversity conservation in the population, which is crucial for preventing premature convergence in multimodal optimization. To enhance the global exploration capability of the FPO algorithm for high multimodality problems, a modiﬁed paralleled virtual team approach is developed for FPO, namely DFPO. The main function of this dynamic virtual team is to build a paralleled information-exchange system, strengthening the swarm's global searching eﬀectiveness. Furthermore, the strategy of team size selection is deﬁned in DFPO named as DFPO-r, which based on the fact that a dynamic virtual team with a higher degree of population diversity is able to help DFPO-r alleviate the premature convergence and strengthen the global exploration simultaneously. Experimental results demonstrate that both DFPO-r and DFPO have desirable performances for multimodal functions. In addition, DFPO-r has a more robust performance in most casescompared with DFPO. Using hybrid algorithms to deal with speciﬁc real-world problems is one of the most interesting trends in the last years. In this thesis, we extend the FPO algorithm for fuzzy clustering optimization problem. Thus, a combination of FPO with FCM (FPO-FCM) algorithm is proposed to avoid the premature convergence and improve the performance of FCM. To handle the large scale global optimization problem, a variant of modiﬁed BBPSO algorithm incorporation of Diﬀerential Evolution (DE) approach, namely BBPSO-DE, is developed to improve the swarm's global search capability as the dimensionality of the search space increases. To the best of our knowledge, the Static Heterogeneous PSO (SHPSO) has been studied by some researchers, while the Dynamic Heterogeneous PSO (DHPSO) is seldom systematically investigated based on real problems. In this thesis, two variants of dynamic Heterogeneous PSO, namely DHPSO-d and DHPSO-p are proposed for complex real-world problems. In DHPSO-d, several diﬀerential update rules are proposed for diﬀerent particles by the trigger event. When the global best position 𝑝𝑔 is considered stagnant and the event is conﬁrmed, then 𝑝𝑔 is reset and all particles update their positions only by their personal experience. In DHPSO-p, two proposed types of topology models provide the particles diﬀerent…

Subjects/Keywords: Particle Swarm Optimization; Global Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

YANG, S. (2017). Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究. (Thesis). Hosei University / 法政大学. Retrieved from http://hdl.handle.net/10114/13303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

YANG, Shiqin. “Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究.” 2017. Thesis, Hosei University / 法政大学. Accessed February 19, 2020. http://hdl.handle.net/10114/13303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

YANG, Shiqin. “Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究.” 2017. Web. 19 Feb 2020.

Vancouver:

YANG S. Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究. [Internet] [Thesis]. Hosei University / 法政大学; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10114/13303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

YANG S. Research on Homogeneous and Heterogeneous Particle Swarm Optimization for Global Optimization Problems : 大域的最適化問題のための同種および異種粒子群最適化法の研究. [Thesis]. Hosei University / 法政大学; 2017. Available from: http://hdl.handle.net/10114/13303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Drosos, Christos. Optimized heuristic implementations in al multi - agent systems.

Degree: 2016, University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς

URL: http://hdl.handle.net/10442/hedi/38763

► A central vision of the combinatorial optimization is to deliver a number of independent lines of operational research by implementing algorithms and hyper-graph theory on… (more)

▼ A central vision of the combinatorial optimization is to deliver a number of independent lines of operational research by implementing algorithms and hyper-graph theory on a series of random theoretical computer science problems. A main motivation is that thousands of real-life problems can be formulated as abstract combinatorial optimization problems. Albeit, multi-agent systems can also be used to solve problems that are difficult or impossible for monolithic systems to solve and methodic, functional, procedural, algorithmic and reinforcement forms of approaches are needed. Thus, in practice, extensive research and development efforts are required when applying combinatorial optimization in intelligent multi-agent systems, in order to achieve cooperation, interoperability and sustainability in heterogeneous and complex existing or future industrial, aerospace, robotic systems or/and any other cyber-physical system. Driven by the above mentioned problems, this Doctoral Dissertation presents meta-heuristic approaches in the area of AI algorithms and specifically the particle swarm and ant colony optimization to solve combinatorial optimization problems. More specifically present thesis’ applications/implementations focus on enhancing the:I.Combinational optimization of the intelligent multi-agent systems;II.Capabilities of the modern and intelligent multi-agent systems;III.Sustainability of the global cyber-physical systems.The methodological approach followed in this Dissertation for reaching the first goal may be described within the following achievements: •Reformulate the problem of dartboard game within the spectrum of metaheuristic algorithms•Use/Apply the Ant Colony System and Max-Min Ant System (MMAS) algorithm as a metaheuristic strategy that guide the search process•Reinforces local search in neighborhood of the best solution found in each iteration. In addition, heuristic is an adjective for experience-based techniques that help in problem solving, learning and discovery and heuristic methods are used to rapidly come to an 'optimal solution'. In more precise terms, heuristics stand for strategies using readily accessible information to control problem solving in many practical scenarios, slowing convergence and facilitating exploration.However, the Ant Colony Optimization technique has emerged recently as a new meta-heuristic for hard combinatorial Optimization Problems. Implementing a randomized construction heuristic extension of Ant Colony System algorithm in the Vehicle Scheduling Problem has become the second goal of this dissertation which is met by the following: •Definition the objective function for VSP •Make probabilistic decisions as a function of artificial pheromone trails •Estimate the effect of varying of levels of initial pheromone quantities on the objective function using an extension of Ant Colony System (ACS) algorithmThe final goal of this thesis is to enhance the previously referred goals/contribution regarding the sustainable development of an application paradigm. All parts included…

Subjects/Keywords: Αλγόριθμοι; Particle swarm optimization

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APA (6^th Edition):

Drosos, C. (2016). Optimized heuristic implementations in al multi - agent systems. (Thesis). University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς. Retrieved from http://hdl.handle.net/10442/hedi/38763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Drosos, Christos. “Optimized heuristic implementations in al multi - agent systems.” 2016. Thesis, University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς. Accessed February 19, 2020. http://hdl.handle.net/10442/hedi/38763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Drosos, Christos. “Optimized heuristic implementations in al multi - agent systems.” 2016. Web. 19 Feb 2020.

Vancouver:

Drosos C. Optimized heuristic implementations in al multi - agent systems. [Internet] [Thesis]. University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς; 2016. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10442/hedi/38763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Drosos C. Optimized heuristic implementations in al multi - agent systems. [Thesis]. University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς; 2016. Available from: http://hdl.handle.net/10442/hedi/38763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Pretoria

4. Cleghorn, Christopher Wesley. A Generalized theoretical deterministic particle swarm model.

Degree: Computer Science, 2013, University of Pretoria

URL: http://hdl.handle.net/2263/33333

► Particle swarm optimization (PSO) is a well known population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized in… (more)

▼ Particle swarm optimization (PSO) is a well known population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized in a variety of application domains, providing a wealth of empirical evidence for its effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent to its inception, some of which are fundamental to the PSO's core behavior, others have been more application specific. The fundamental alterations to the PSO have to a large extent been a result of theoretical analysis of the PSO's particle's long term trajectory. The most obvious example, is the need for velocity clamping in the original PSO. While there were empirical fndings that suggested that each particle's velocity was increasing at a rapid rate, it was only once a solid theoretical study was performed that the reason for the velocity explosion was understood. There has been a large amount of theoretical research done on the PSO, both for the deterministic model, and more recently for the stochastic model. This thesis presents an extension to the theoretical deterministic PSO model. Under the extended model, conditions for particle convergence to a point are derived. At present all theoretical PSO research is done under the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO's behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. The model used in this thesis greatly weakens the stagnation assumption, by instead assuming that each particle's personal best and neighborhood best can occupy an arbitrarily large number of unique positions. Empirical results are presented to support the theoretical fndings. Advisors/Committee Members: Engelbrecht, Andries P. (advisor).

Subjects/Keywords: Particle swarm optimization (PSO); UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cleghorn, C. W. (2013). A Generalized theoretical deterministic particle swarm model. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/33333

Chicago Manual of Style (16^th Edition):

Cleghorn, Christopher Wesley. “A Generalized theoretical deterministic particle swarm model.” 2013. Masters Thesis, University of Pretoria. Accessed February 19, 2020. http://hdl.handle.net/2263/33333.

MLA Handbook (7^th Edition):

Cleghorn, Christopher Wesley. “A Generalized theoretical deterministic particle swarm model.” 2013. Web. 19 Feb 2020.

Vancouver:

Cleghorn CW. A Generalized theoretical deterministic particle swarm model. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2263/33333.

Council of Science Editors:

Cleghorn CW. A Generalized theoretical deterministic particle swarm model. [Masters Thesis]. University of Pretoria; 2013. Available from: http://hdl.handle.net/2263/33333

University of Ontario Institute of Technology

5. Ali, Mohammad Yasin. Energy management system of a microgrid with distributed generation.

Degree: 2019, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/1094

► An Energy Management System (EMS) is required to control the flow of power and match generation with the load within a microgrid during grid-connected and… (more)

Subjects/Keywords: Particle swarm optimization; Differential evolution

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ali, M. Y. (2019). Energy management system of a microgrid with distributed generation. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1094

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ali, Mohammad Yasin. “Energy management system of a microgrid with distributed generation.” 2019. Thesis, University of Ontario Institute of Technology. Accessed February 19, 2020. http://hdl.handle.net/10155/1094.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ali, Mohammad Yasin. “Energy management system of a microgrid with distributed generation.” 2019. Web. 19 Feb 2020.

Vancouver:

Ali MY. Energy management system of a microgrid with distributed generation. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10155/1094.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ali MY. Energy management system of a microgrid with distributed generation. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1094

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

6. Lakshmipathy, Sathish Kumar. VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188

► Improving core performance by reshuffling/reloading the fuel blocks within the core is one of the in-core fuel management methods with two major benefits: a possibility… (more)

▼ Improving core performance by reshuffling/reloading the fuel blocks within the core is one of the in-core fuel management methods with two major benefits: a possibility to improve core life and increase core safety. VHTR is a hexagonal annular core reactor with reflectors in the center and outside the fuel rings (3-rings). With the block type fuel assemblies, there is an opportunity for muti-dimensional fuel bocks movement within the core during scheduled reactor refueling operations. As the core is symmetric, by optimizing the shuffle operation of 1/6th of the core, the same process can be repeated through the remaining 5/6th of the core. VHTR has 170 fuel blocks in the core of which 50 are control rod blocks and are not movable to regular fuel block locations. The reshuffling problem now is to find the best combination of 120 fuel blocks that has a minimized power peaking and/or increased core life under safety constraints among the 120! combinations. For evaluating each LP during the shuffling, a fitness function that is developed from the parameters affecting the power peaking and core life is required. Calculating the power peaking at each step using Monte Carlo simulations on a whole core exact geometry model is a time consuming process and not feasible. A parameter is developed from the definitions of reactivity and power peaking factor called the localized reactivity potential that can be estimated for every block movement based on the reaction rates and atom densities of the initial core burnup at the time of shuffling. The algorithm (ReloPSO) is based on Particle Swarm Optimization algorithm the search process by improving towards the optimum from a set of random LPs based on the fitness function developed with the reactivity potential parameter. The algorithm works as expected and the output obtained has a flatter reactivity profile than the input. The core criticality is found to increase when shuffled closer to end of life. Detailed analysis on the burn runs after shuffling at different time of core operation is required to correlate the estimated and actual values of the reactivity parameter and to optimize the time of shuffle. Advisors/Committee Members: Tsvetkov, Pavel V. (advisor), Hassan, Yassin A. (committee member), Curry, Guy L. (committee member).

Subjects/Keywords: Particle Swarm Optimization; Core shuffling optimization

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APA (6^th Edition):

Lakshmipathy, S. K. (2012). VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lakshmipathy, Sathish Kumar. “VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D.” 2012. Thesis, Texas A&M University. Accessed February 19, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lakshmipathy, Sathish Kumar. “VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D.” 2012. Web. 19 Feb 2020.

Vancouver:

Lakshmipathy SK. VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lakshmipathy SK. VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

7. Pehlivanoglu, Yasin Volkan. Hybrid Intelligent Optimization Methods for Engineering Problems.

Degree: PhD, Aerospace Engineering, 2010, Old Dominion University

URL: 9781124113166 ; https://digitalcommons.odu.edu/mae_etds/83

► The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution… (more)

▼ The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation… Advisors/Committee Members: Oktay Baysal, Ali Beskok, Gene Hou, Abdurrahman Hacioglu.

Subjects/Keywords: Intelligent optimization; Particle swarm optimization; Aerospace Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pehlivanoglu, Y. V. (2010). Hybrid Intelligent Optimization Methods for Engineering Problems. (Doctoral Dissertation). Old Dominion University. Retrieved from 9781124113166 ; https://digitalcommons.odu.edu/mae_etds/83

Chicago Manual of Style (16^th Edition):

Pehlivanoglu, Yasin Volkan. “Hybrid Intelligent Optimization Methods for Engineering Problems.” 2010. Doctoral Dissertation, Old Dominion University. Accessed February 19, 2020. 9781124113166 ; https://digitalcommons.odu.edu/mae_etds/83.

MLA Handbook (7^th Edition):

Pehlivanoglu, Yasin Volkan. “Hybrid Intelligent Optimization Methods for Engineering Problems.” 2010. Web. 19 Feb 2020.

Vancouver:

Pehlivanoglu YV. Hybrid Intelligent Optimization Methods for Engineering Problems. [Internet] [Doctoral dissertation]. Old Dominion University; 2010. [cited 2020 Feb 19]. Available from: 9781124113166 ; https://digitalcommons.odu.edu/mae_etds/83.

Council of Science Editors:

Pehlivanoglu YV. Hybrid Intelligent Optimization Methods for Engineering Problems. [Doctoral Dissertation]. Old Dominion University; 2010. Available from: 9781124113166 ; https://digitalcommons.odu.edu/mae_etds/83

University of Saskatchewan

8. Voss, Krzysztof M. pythOPT: A problem-solving environment for optimization methods.

Degree: 2017, University of Saskatchewan

URL: http://hdl.handle.net/10388/7746

► Optimization is a process of finding the best solutions to problems based on mathematical models. There are numerous methods for solving optimization problems, and there… (more)

▼ Optimization is a process of finding the best solutions to problems based on mathematical models. There are numerous methods for solving optimization problems, and there is no method that is superior for all problems. This study focuses on the Particle Swarm Optimization (PSO) family of methods, which is based on the swarm behaviour of biological organisms. These methods are easily adjustable, scalable, and have been proven successful in solving optimization problems. This study examines the performance of nine optimization methods on four sets of problems. The performance analysis of these methods is based on two performance metrics (the win-draw-loss metric and the performance profiles metric) that are used to analyze experimental data. The data are gathered by using each optimization method in multiple configurations to solve four classes of problems. A software package pythOPT was created. It is a problem-solving environment that is comprised of a library, a framework, and a system for benchmarking optimization methods. pythOPT includes code that prepares experiments, executes computations on a distributed system, stores results in a database, analyzes those results, and visualizes analyses. It also includes a framework for building PSO-based methods and a library of benchmark functions used in one of the presented analyses. Using pythOPT, the performance of these nine methods is compared in relation to three parameters: number of available function evaluations, accuracy of solutions, and communication topology. This experiment demonstrates that two methods (SPSO and GCPSO) are superior in finding solutions for the tested classes of problems. Finally, by using pythOPT we can recreate this study and produce similar ones by changing the parameters of an experiment. We can add new methods and evaluate their performances, and this helps in developing new optimization methods. Advisors/Committee Members: Spiteri, Raymond, Eramian, Mark, Horsch, Michael, Steele, Tom.

Subjects/Keywords: optimization; pso; particle swarm optimization; direct; benchmarking

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APA (6^th Edition):

Voss, K. M. (2017). pythOPT: A problem-solving environment for optimization methods. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7746

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Voss, Krzysztof M. “pythOPT: A problem-solving environment for optimization methods.” 2017. Thesis, University of Saskatchewan. Accessed February 19, 2020. http://hdl.handle.net/10388/7746.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Voss, Krzysztof M. “pythOPT: A problem-solving environment for optimization methods.” 2017. Web. 19 Feb 2020.

Vancouver:

Voss KM. pythOPT: A problem-solving environment for optimization methods. [Internet] [Thesis]. University of Saskatchewan; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10388/7746.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Voss KM. pythOPT: A problem-solving environment for optimization methods. [Thesis]. University of Saskatchewan; 2017. Available from: http://hdl.handle.net/10388/7746

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Subramanian M. A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;.

Degree: A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy, 2015, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/38600

►

An end milling process is a multipoint interrupted cutting in newlinewhich the contact between cutting edge and the work piece is not continuous newlineand the… (more)

Subjects/Keywords: Genetic Algorithm; Particle Swarm Optimization; Simulated Annealing

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APA (6^th Edition):

M, S. (2015). A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/38600

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

M, Subramanian. “A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;.” 2015. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/38600.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

M, Subramanian. “A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;.” 2015. Web. 19 Feb 2020.

Vancouver:

M S. A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38600.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

M S. A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38600

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

10. Stanley, Aishwarya. Identifying near-earth asteroid targets for human exploration using particle swarm optimization.

Degree: MS, 4048, 2013, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/44435

► The goal of this thesis is to identify potential Near-Earth Asteroids (NEAs) that are viable candidates for human exploration. The computational method incorporated to help… (more)

Subjects/Keywords: Near-Earth Asteroids; Particle Swarm Optimization

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APA (6^th Edition):

Stanley, A. (2013). Identifying near-earth asteroid targets for human exploration using particle swarm optimization. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44435

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Stanley, Aishwarya. “Identifying near-earth asteroid targets for human exploration using particle swarm optimization.” 2013. Thesis, University of Illinois – Urbana-Champaign. Accessed February 19, 2020. http://hdl.handle.net/2142/44435.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Stanley, Aishwarya. “Identifying near-earth asteroid targets for human exploration using particle swarm optimization.” 2013. Web. 19 Feb 2020.

Vancouver:

Stanley A. Identifying near-earth asteroid targets for human exploration using particle swarm optimization. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2013. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2142/44435.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stanley A. Identifying near-earth asteroid targets for human exploration using particle swarm optimization. [Thesis]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44435

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brock University

11. Bond, Ron. Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms .

Degree: Department of Computer Science, Brock University

URL: http://hdl.handle.net/10464/6314

► Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find… (more)

▼ Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Subjects/Keywords: Particle swarm optimization

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APA (6^th Edition):

Bond, R. (n.d.). Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms . (Thesis). Brock University. Retrieved from http://hdl.handle.net/10464/6314

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bond, Ron. “Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms .” Thesis, Brock University. Accessed February 19, 2020. http://hdl.handle.net/10464/6314.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bond, Ron. “Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms .” Web. 19 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Bond R. Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms . [Internet] [Thesis]. Brock University; [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10464/6314.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Bond R. Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms . [Thesis]. Brock University; Available from: http://hdl.handle.net/10464/6314

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Brigham Young University

12. McNabb, Andrew W. Parallel Particle Swarm Optimization and Large Swarms.

Degree: MS, 2011, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3479&context=etd

► Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm,… (more)

▼ Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take hours or days to optimize difficult objective functions which are deceptive or expensive. Deceptive functions may be highly multimodal and multidimensional, and PSO requires extensive exploration to avoid being trapped in local optima. Expensive functions, whose computational complexity may arise from dependence on detailed simulations or large datasets, take a long time to evaluate. For deceptive or expensive objective functions, PSO must be parallelized to use multiprocessor systems and clusters efficiently. This thesis investigates the implications of parallelizing PSO and in particular, the details of parallelization and the effects of large swarms. PSO can be expressed naturally in Google's MapReduce framework to develop a simple and robust parallel implementation that automatically includes communication, load balancing, and fault tolerance. This flexible implementation makes it easy to apply modifications to the algorithm, such as those that improve optimization of difficult objective functions and improve parallel performance. Results show that larger swarms help with both of these goals, but they are most effective if arranged into sparse topologies with lower overhead from communication. Additionally, PSO must be modified to use communication more efficiently in a large sparse swarm for objective functions where information ideally flows quickly through a large swarm. Swarm size is usually fixed at a modest number around 50, but particularly in a parallel computational environment, much larger swarms are much more effective for deceptive objective functions. Likewise, swarms much smaller than 50 are more effective for expensive but less deceptive functions. In general, swarm size should be carefully chosen using all available information about the objective function and computational environment.

Subjects/Keywords: particle swarm optimization; parallelization; Computer Sciences

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APA (6^th Edition):

McNabb, A. W. (2011). Parallel Particle Swarm Optimization and Large Swarms. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3479&context=etd

Chicago Manual of Style (16^th Edition):

McNabb, Andrew W. “Parallel Particle Swarm Optimization and Large Swarms.” 2011. Masters Thesis, Brigham Young University. Accessed February 19, 2020. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3479&context=etd.

MLA Handbook (7^th Edition):

McNabb, Andrew W. “Parallel Particle Swarm Optimization and Large Swarms.” 2011. Web. 19 Feb 2020.

Vancouver:

McNabb AW. Parallel Particle Swarm Optimization and Large Swarms. [Internet] [Masters thesis]. Brigham Young University; 2011. [cited 2020 Feb 19]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3479&context=etd.

Council of Science Editors:

McNabb AW. Parallel Particle Swarm Optimization and Large Swarms. [Masters Thesis]. Brigham Young University; 2011. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3479&context=etd

University of Waterloo

13. El-Abd, Mohammed. Cooperative Models of Particle Swarm Optimizers.

Degree: 2008, University of Waterloo

URL: http://hdl.handle.net/10012/3958

► Particle Swarm Optimization (PSO) is one of the most effFective optimization tools, which emerged in the last decade. Although, the original aim was to simulate… (more)

▼ Particle Swarm Optimization (PSO) is one of the most effFective optimization tools, which emerged in the last decade. Although, the original aim was to simulate the behavior of a group of birds or a school of fish looking for food, it was quickly realized that it could be applied in optimization problems. Different directions have been taken to analyze the PSO behavior as well as improving its performance. One approach is the introduction of the concept of cooperation. This thesis focuses on studying this concept in PSO by investigating the different design decisions that influence the cooperative PSO models' performance and introducing new approaches for information exchange. Firstly, a comprehensive survey of all the cooperative PSO models proposed in the literature is compiled and a definition of what is meant by a cooperative PSO model is introduced. A taxonomy for classifying the different surveyed cooperative PSO models is given. This taxonomy classifies the cooperative models based on two different aspects: the approach the model uses for decomposing the problem search space and the method used for placing the particles into the different cooperating swarms. The taxonomy helps in gathering all the proposed models under one roof and understanding the similarities and differences between these models. Secondly, a number of parameters that control the performance of cooperative PSO models are identified. These parameters give answers to the four questions: Which information to share? When to share it? Whom to share it with? and What to do with it? A complete empirical study is conducted on one of the cooperative PSO models in order to understand how the performance changes under the influence of these parameters. Thirdly, a new heterogeneous cooperative PSO model is proposed, which is based on the exchange of probability models rather than the classical migration of particles. The model uses two swarms that combine the ideas of PSO and Estimation of Distribution Algorithms (EDAs) and is considered heterogeneous since the cooperating swarms use different approaches to sample the search space. The model is tested using different PSO models to ensure that the performance is robust against changing the underlying population topology. The experiments show that the model is able to produce better results than its components in many cases. The model also proves to be highly competitive when compared to a number of state-of-the-art cooperative PSO algorithms. Finally, two different versions of the PSO algorithm are applied in the FPGA placement problem. One version is applied entirely in the discrete domain, which is the first attempt to solve this problem in this domain using a discrete PSO (DPSO). Another version is implemented in the continuous domain. The PSO algorithms are applied to several well-known FPGA benchmark problems with increasing dimensionality. The results are compared to those obtained by the academic Versatile Place and Route (VPR) placement tool, which is based on Simulated Annealing…

Subjects/Keywords: Particle Swarm Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

El-Abd, M. (2008). Cooperative Models of Particle Swarm Optimizers. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/3958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

El-Abd, Mohammed. “Cooperative Models of Particle Swarm Optimizers.” 2008. Thesis, University of Waterloo. Accessed February 19, 2020. http://hdl.handle.net/10012/3958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

El-Abd, Mohammed. “Cooperative Models of Particle Swarm Optimizers.” 2008. Web. 19 Feb 2020.

Vancouver:

El-Abd M. Cooperative Models of Particle Swarm Optimizers. [Internet] [Thesis]. University of Waterloo; 2008. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10012/3958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

El-Abd M. Cooperative Models of Particle Swarm Optimizers. [Thesis]. University of Waterloo; 2008. Available from: http://hdl.handle.net/10012/3958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Manitoba

14. Franz, Wayne. Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition.

Degree: Computer Science, 2013, University of Manitoba

URL: http://hdl.handle.net/1993/23842

► Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population… (more)

Subjects/Keywords: Particle Swarm Optimization; Genetic Algorithm; GPU; Metaheuristic

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APA (6^th Edition):

Franz, W. (2013). Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/23842

Chicago Manual of Style (16^th Edition):

Franz, Wayne. “Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition.” 2013. Masters Thesis, University of Manitoba. Accessed February 19, 2020. http://hdl.handle.net/1993/23842.

MLA Handbook (7^th Edition):

Franz, Wayne. “Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition.” 2013. Web. 19 Feb 2020.

Vancouver:

Franz W. Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition. [Internet] [Masters thesis]. University of Manitoba; 2013. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1993/23842.

Council of Science Editors:

Franz W. Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition. [Masters Thesis]. University of Manitoba; 2013. Available from: http://hdl.handle.net/1993/23842

University of Illinois – Chicago

15. Shah, Rahul H. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/21993

► Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers… (more)

▼ Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine… Advisors/Committee Members: Li, Lin (advisor), Hu, Mengqi (committee member), Williams, Quintin (committee member), Li, Lin (chair).

Subjects/Keywords: Particle Swarm Optimization; Energy; Maintenance; Scheduling

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APA (6^th Edition):

Shah, R. H. (2017). Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21993

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Shah, Rahul H. “Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization.” 2017. Thesis, University of Illinois – Chicago. Accessed February 19, 2020. http://hdl.handle.net/10027/21993.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Shah, Rahul H. “Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization.” 2017. Web. 19 Feb 2020.

Vancouver:

Shah RH. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10027/21993.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shah RH. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21993

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidad de Chile

16. Lai, Chun-Hau. Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO .

Degree: 2012, Universidad de Chile

URL: http://www.repositorio.uchile.cl/handle/2250/111954

► Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado,… (more)

▼ Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado, el cual consiste en dividir un conjunto de n puntos en k clusters tal que cada cluster tenga como m ́ınimo ⌊ n ⌋ puntos, k y éstos deben estar lo más cercano posible al centroide de cada cluster. Estudiamos los algoritmos existentes para este problema y nuestro análisis muestra que éstos podrían fallar en entregar un resultado óptimo por la ausencia de la evaluación de los resultados en cada iteración del algoritmo. Entonces, recurrimos al concepto de Particles Swarms, que fue introducido inicialmente para simular el comportamiento social humano y que permite explorar todas las posibles soluciones de manera que se aproximen a la óptima rápidamente. Proponemos cuatro algoritmos basado en Particle Swarm Optimization (PSO): PSO-Hu ́ngaro, PSO-Gale-Shapley, PSO-Aborci ́on-Punto-Cercano y PSO-Convex-Hull, que aprovechan la característica de la generación aleatoria de los centroides por el algoritmo PSO, para asignar los puntos a estos centroides, logrando una solución más aproximada a la óptima. Evaluamos estos cuatro algoritmos con conjuntos de datos distribuidos en forma uniforme y no uniforme. Se encontró que para los conjuntos de datos distribuidos no uniformemente, es impredecible determinar cuál de los cuatro algoritmos propuestos llegaría a tener un mejor resultado de acuerdo al conjunto de métricas (intra-cluster-distancia, índice Davies-Doublin e índice Dunn). Por eso, nos concentramos con profundidad en el comportamiento de ellos para los conjuntos de datos distribuidos en forma uniforme. Durante el proceso de evaluación se descubrió que la formación de los clusters balanceados de los algoritmos PSO-Absorcion-Puntos-Importantes y PSO-Convex-Hull depende fuertemente del orden con que los centroides comienzan a absorber los puntos más cercanos. En cambio, los algoritmos PSO-Hungaro y PSO-Gale-Shapley solamente dependen de los centroides generados y no del orden de los clusters a crear. Se pudo concluir que el algoritmo PSO-Gale-Shapley presenta el rendimiento menos bueno para la creación de clusters balanceados, mientras que el algoritmo PSO-Hungaro presenta el rendimiento más eficiente para lograr el resultado esperado. Éste último está limitado al tamaño de los datos y la forma de distribución. Se descubrió finalmente que, para los conjuntos de datos de tamaños grandes, independiente de la forma de distribución, el algoritmo PSO-Convex-Hull supera a los demás, entregando mejor resultado según las métricas usadas. Advisors/Committee Members: Gutiérrez Gallardo, Claudio (advisor).

Subjects/Keywords: Algoritmos computacionales; Clustering balancead; Particle swarm optimization

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APA (6^th Edition):

Lai, C. (2012). Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO . (Thesis). Universidad de Chile. Retrieved from http://www.repositorio.uchile.cl/handle/2250/111954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lai, Chun-Hau. “Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO .” 2012. Thesis, Universidad de Chile. Accessed February 19, 2020. http://www.repositorio.uchile.cl/handle/2250/111954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lai, Chun-Hau. “Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO .” 2012. Web. 19 Feb 2020.

Vancouver:

Lai C. Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO . [Internet] [Thesis]. Universidad de Chile; 2012. [cited 2020 Feb 19]. Available from: http://www.repositorio.uchile.cl/handle/2250/111954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lai C. Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO . [Thesis]. Universidad de Chile; 2012. Available from: http://www.repositorio.uchile.cl/handle/2250/111954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

IUPUI

17. Chong Chie, Juan Antonio Kim Hoo. Motion correction of PET/CT images.

Degree: 2017, IUPUI

URL: http://hdl.handle.net/1805/12353

►

Indiana University-Purdue University Indianapolis (IUPUI)

The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron… (more)

Subjects/Keywords: PET/CT; Motion Correction; Particle Swarm Optimization

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APA (6^th Edition):

Chong Chie, J. A. K. H. (2017). Motion correction of PET/CT images. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/12353

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chong Chie, Juan Antonio Kim Hoo. “Motion correction of PET/CT images.” 2017. Thesis, IUPUI. Accessed February 19, 2020. http://hdl.handle.net/1805/12353.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chong Chie, Juan Antonio Kim Hoo. “Motion correction of PET/CT images.” 2017. Web. 19 Feb 2020.

Vancouver:

Chong Chie JAKH. Motion correction of PET/CT images. [Internet] [Thesis]. IUPUI; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1805/12353.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chong Chie JAKH. Motion correction of PET/CT images. [Thesis]. IUPUI; 2017. Available from: http://hdl.handle.net/1805/12353

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Anna University

18. Jackuline moni D. Certain optimization techniques for Floorplanning in vlsi physical design;.

Degree: Certain optimization techniques for Floorplanning in vlsi physical design, 2014, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/27186

►

newlineFloorplanning is an important stage in VLSI Very Large Scale newlineIntegrated Circuits physical design cycle as it determines the performance newlinesize and reliability of VLSI… (more)

Subjects/Keywords: Differential Evolution; Hybrid Particle Swarm Optimization; Particle Swarm Optimization; Very Large Scale Integrated

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APA (6^th Edition):

D, J. m. (2014). Certain optimization techniques for Floorplanning in vlsi physical design;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/27186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

D, Jackuline moni. “Certain optimization techniques for Floorplanning in vlsi physical design;.” 2014. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/27186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

D, Jackuline moni. “Certain optimization techniques for Floorplanning in vlsi physical design;.” 2014. Web. 19 Feb 2020.

Vancouver:

D Jm. Certain optimization techniques for Floorplanning in vlsi physical design;. [Internet] [Thesis]. Anna University; 2014. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

D Jm. Certain optimization techniques for Floorplanning in vlsi physical design;. [Thesis]. Anna University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

19. Komarasamy G. Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;.

Degree: Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets, 2015, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/37796

►

Data mining is a process of extracting knowledge from homogeneous newlinewide variety of datasets It is mainly used in interdisciplinary subfield namely newlineartificial intelligence machine… (more)

Subjects/Keywords: Data mining; K means; K Means Particle Swarm Optimization; Particle Swarm Optimization

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APA (6^th Edition):

G, K. (2015). Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/37796

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

G, Komarasamy. “Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;.” 2015. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/37796.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

G, Komarasamy. “Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;.” 2015. Web. 19 Feb 2020.

Vancouver:

G K. Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/37796.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

G K. Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/37796

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

20. PRATHYUSHA, YERRA. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

► The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs… (more)

Subjects/Keywords: Swarm intelligence; UAV; Particle Swarm Optimization (PSO); Navigation algorithm; Path planning

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APA (6^th Edition):

PRATHYUSHA, Y. (2018). UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Thesis, NSYSU. Accessed February 19, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Web. 19 Feb 2020.

Vancouver:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 19]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

21. Talukder, Satyobroto. Mathematicle Modelling and Applications of Particle Swarm Optimization.

Degree: 2011, , School of Engineering

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

► Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or… (more)

Subjects/Keywords: Optimization; swarm intelligence; particle swarm; social network; convergence; stagnation; multi-objective.

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APA (6^th Edition):

Talukder, S. (2011). Mathematicle Modelling and Applications of Particle Swarm Optimization. (Thesis). , School of Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Thesis, , School of Engineering. Accessed February 19, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Web. 19 Feb 2020.

Vancouver:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2020 Feb 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Thesis]. , School of Engineering; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oklahoma State University

22. Daneshyari, Moayed. Cultural Particle Swarm Optimization.

Degree: School of Electrical & Computer Engineering, 2010, Oklahoma State University

URL: http://hdl.handle.net/11244/7852

► In this dissertation, a computational paradigm is proposed that adopts the particle swarm optimization within a cultural framework. The cultural algorithm adopted here consists of… (more)

Subjects/Keywords: constrained optimization; cultural algorithm; dynamic optimization; multiobjective optimization; particle swarm o

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APA (6^th Edition):

Daneshyari, M. (2010). Cultural Particle Swarm Optimization. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/7852

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Daneshyari, Moayed. “Cultural Particle Swarm Optimization.” 2010. Thesis, Oklahoma State University. Accessed February 19, 2020. http://hdl.handle.net/11244/7852.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Daneshyari, Moayed. “Cultural Particle Swarm Optimization.” 2010. Web. 19 Feb 2020.

Vancouver:

Daneshyari M. Cultural Particle Swarm Optimization. [Internet] [Thesis]. Oklahoma State University; 2010. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/11244/7852.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Daneshyari M. Cultural Particle Swarm Optimization. [Thesis]. Oklahoma State University; 2010. Available from: http://hdl.handle.net/11244/7852

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

De Montfort University

23. Al-Obaidi, Mohanad. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.

Degree: PhD, 2010, De Montfort University

URL: http://hdl.handle.net/2086/5187

► Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm… (more)

▼ Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.

Subjects/Keywords: 600; genetic algorithms; clustering; evolutionary computation; sensor networks; swarm intelligence; energy optimization; particle swarm optimization

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APA (6^th Edition):

Al-Obaidi, M. (2010). ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/5187

Chicago Manual of Style (16^th Edition):

Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Doctoral Dissertation, De Montfort University. Accessed February 19, 2020. http://hdl.handle.net/2086/5187.

MLA Handbook (7^th Edition):

Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Web. 19 Feb 2020.

Vancouver:

Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Internet] [Doctoral dissertation]. De Montfort University; 2010. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2086/5187.

Council of Science Editors:

Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Doctoral Dissertation]. De Montfort University; 2010. Available from: http://hdl.handle.net/2086/5187

24. Kelman, Alexander. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.

Degree: Informatics, 2017, University of Skövde

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

► The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess… (more)

Subjects/Keywords: Swarm Intelligence; Pathfinding; Ant Colony Optimization; Particle Swarm Optimization; A*; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Kelman, A. (2017). Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Thesis, University of Skövde. Accessed February 19, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Web. 19 Feb 2020.

Vancouver:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2020 Feb 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Thesis]. University of Skövde; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Saskatchewan

25. Schoene, Thomas. Step-Optimized Particle Swarm Optimization.

Degree: 2011, University of Saskatchewan

URL: http://hdl.handle.net/10388/ETD-2011-08-90

► Particle swarm optimization (PSO) is widely used in industrial and academic research to solve optimization problems. Recent developments of PSO show a direction towards adaptive… (more)

Subjects/Keywords: step-optimized particle swarm optimization; adaptive particle swarm optimization; particle swarm optimization; SOPSO; APSO; PSO; optimization; velocity weights; step-optimized; optimized; adaptive

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Schoene, T. (2011). Step-Optimized Particle Swarm Optimization. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/ETD-2011-08-90

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Schoene, Thomas. “Step-Optimized Particle Swarm Optimization.” 2011. Thesis, University of Saskatchewan. Accessed February 19, 2020. http://hdl.handle.net/10388/ETD-2011-08-90.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Schoene, Thomas. “Step-Optimized Particle Swarm Optimization.” 2011. Web. 19 Feb 2020.

Vancouver:

Schoene T. Step-Optimized Particle Swarm Optimization. [Internet] [Thesis]. University of Saskatchewan; 2011. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10388/ETD-2011-08-90.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Schoene T. Step-Optimized Particle Swarm Optimization. [Thesis]. University of Saskatchewan; 2011. Available from: http://hdl.handle.net/10388/ETD-2011-08-90

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

26. David Lopez Mejia, Victor. A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem.

Degree: Master, IMEPE, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713118-191039

► The last two decades have seen the advent of metaheuristic-based methods to solve both continuous and discrete optimization problems. While there are many paradigms available,… (more)

▼ The last two decades have seen the advent of metaheuristic-based methods to solve both continuous and discrete optimization problems. While there are many paradigms available, the Particle Swarm Optimization (PSO) algorithm has had a profound impact in the mathematical optimization field. Extensive efforts, often with good results, have been made in the literature to improve the performance of the PSO algorithm by modifying the inertia weight, the position, and the velocity update equations. While these equations are still fundamental in its discrete counterpart, the Binary Particle Swarm Optimization (BPSO) algorithm, they are secondary to the velocity mapping procedure, which is essential in the discrete optimization process. Therefore, this thesis proposes a Modified Binary Particle Swarm Optimization algorithm (VS-BPSO) to solve the first stage of the forward day-ahead Thermal Unit Commitment Problem by considering two families of velocity mapping functions, namely the S-Shaped and V-shaped families. A novel pivot heuristic repair algorithm which effectively handles the conflict between system-wide and time-dependent constraints, is used to obtain a feasible Unit Commitment (UC) schedule. The Economic Dispatch (ED) subproblem is then solved for each time slot of the study period with an Enhanced Lambda Iteration Method (ELI). The viability of the proposed solution methodology is simulated in the MATLAB Â© environment and is verified with the use of two benchmark systems, namely the IEEE 10-unit and the IEEE 26-unit test case systems. The obtained numerical results demonstrate the enhanced performance of the VS-BPSO algorithm in terms of total cost and computational time when compared to other methods in the recent literature. Furthermore, three additional cases involving other constraints and objectives are included. Comprehensive discussion of the obtained results is provided. Advisors/Committee Members: Hong Zhan Jin (chair), Whei Min Lin (committee member), Chih-Ming Hung (chair), Tung-Sheng Zhan (chair).

Subjects/Keywords: Unit Commitment; Economic Dispatch; Binary Particle Swarm Optimization; Heuristic; Particle Swarm Optimization; Discrete Optimization; Mapping function

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

David Lopez Mejia, V. (2018). A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713118-191039

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

David Lopez Mejia, Victor. “A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem.” 2018. Thesis, NSYSU. Accessed February 19, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713118-191039.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

David Lopez Mejia, Victor. “A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem.” 2018. Web. 19 Feb 2020.

Vancouver:

David Lopez Mejia V. A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 19]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713118-191039.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

David Lopez Mejia V. A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713118-191039

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Anna University

27. Narmadha, A.K. An exploration on adoption of inventory optimization using Metaheuristic techniques;.

Degree: 2013, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/11552

►

A key concern for global manufacturers today is reducing inventory and inventory driven costs across their supply and distribution networks. Therefore inventory management is considered… (more)

Subjects/Keywords: Inventory optimization; metaheuristic techniques; genetic algorithm; particle swarm optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Narmadha, A. K. (2013). An exploration on adoption of inventory optimization using Metaheuristic techniques;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/11552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Narmadha, A K. “An exploration on adoption of inventory optimization using Metaheuristic techniques;.” 2013. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/11552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Narmadha, A K. “An exploration on adoption of inventory optimization using Metaheuristic techniques;.” 2013. Web. 19 Feb 2020.

Vancouver:

Narmadha AK. An exploration on adoption of inventory optimization using Metaheuristic techniques;. [Internet] [Thesis]. Anna University; 2013. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/11552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Narmadha AK. An exploration on adoption of inventory optimization using Metaheuristic techniques;. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/11552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Anna University

28. Soundarrajan A. Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems.

Degree: Electrical and Electronics, 2012, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/9828

►

Power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. The control of frequency is… (more)

▼

Power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. The control of frequency is achieved primarily through speed governor mechanism aided by Load Frequency control (LFC) for precise control. The Automatic Voltage Regulator (AVR) senses the terminal voltage and adjusts the excitation to maintain a constant terminal voltage. The performance index adopted for problem formulation is settling time, overshoot and oscillations. The primary design goal is to obtain good load disturbance response by optimally selecting the PID controller parameters. To reduce the complexity in tuning PID parameters, Evolutionary computation techniques can be used to solve a wide range of practical problems including optimization and design of PID gains. Intelligent computing techniques like Fuzzy, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Hybrid Evolutionary Algorithms (EA) have been employed to efficiently search global optimum solutions. In this research, efficient optimization algorithms are proposed to tune the optimal gains of PID controllers used for LFC and AVR of Power generating systems. As evident from the graphs and empirical results, the suggested algorithms performed well under changing loads and regulations. The simulation results are found to be satisfactory when compared for settling time, overshoot and oscillations with conventional fixed gain controllers. The combined and synergic use of information yields a promising tool in solving power system control problems that requires optimization of more parameters. Hence, the balance between electric power generation and load demand is achieved satisfactorily, so the voltage magnitude and frequency are maintained at desired level. As a result the quality and reliability in the power system is improved by the application of Evolutionary Algorithms.

Appendix p. 246-262

Advisors/Committee Members: Sumathi S.

Subjects/Keywords: Genetic Algorithm; Particle Swarm Optimization; Ant Colony Optimization; Hybrid Evolutionary Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

A, S. (2012). Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/9828

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

A, Soundarrajan. “Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems.” 2012. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/9828.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

A, Soundarrajan. “Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems.” 2012. Web. 19 Feb 2020.

Vancouver:

A S. Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems. [Internet] [Thesis]. Anna University; 2012. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9828.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

A S. Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems. [Thesis]. Anna University; 2012. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9828

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

29. Baggett, Benjamin Matthew Wall. Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications.

Degree: MS, Electrical and Computer Engineering, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/32532

► Over the years, phased array antennas have provided electronic scanning with high gain and low sidelobe levels for many radar and satellite applications. The need… (more)

Subjects/Keywords: optimization; helix; phased arrays; wideband; aperiodic; Particle Swarm Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Baggett, B. M. W. (2011). Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32532

Chicago Manual of Style (16^th Edition):

Baggett, Benjamin Matthew Wall. “Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications.” 2011. Masters Thesis, Virginia Tech. Accessed February 19, 2020. http://hdl.handle.net/10919/32532.

MLA Handbook (7^th Edition):

Baggett, Benjamin Matthew Wall. “Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications.” 2011. Web. 19 Feb 2020.

Vancouver:

Baggett BMW. Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10919/32532.

Council of Science Editors:

Baggett BMW. Optimization of Aperiodically Spaced Antenna Arrays for Wideband Applications. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/32532

Brigham Young University

30. Gardner, Matthew J. A Speculative Approach to Parallelization in Particle Swarm Optimization.

Degree: MS, 2011, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4011&context=etd

► Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a… (more)

Subjects/Keywords: parallel algorithms; optimization methods; particle swarm optimization; speculative decomposition; Computer Sciences

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APA (6^th Edition):

Gardner, M. J. (2011). A Speculative Approach to Parallelization in Particle Swarm Optimization. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4011&context=etd

Chicago Manual of Style (16^th Edition):

Gardner, Matthew J. “A Speculative Approach to Parallelization in Particle Swarm Optimization.” 2011. Masters Thesis, Brigham Young University. Accessed February 19, 2020. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4011&context=etd.

MLA Handbook (7^th Edition):

Gardner, Matthew J. “A Speculative Approach to Parallelization in Particle Swarm Optimization.” 2011. Web. 19 Feb 2020.

Vancouver:

Gardner MJ. A Speculative Approach to Parallelization in Particle Swarm Optimization. [Internet] [Masters thesis]. Brigham Young University; 2011. [cited 2020 Feb 19]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4011&context=etd.

Council of Science Editors:

Gardner MJ. A Speculative Approach to Parallelization in Particle Swarm Optimization. [Masters Thesis]. Brigham Young University; 2011. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4011&context=etd

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