subject:(Vector Evaluated Particle Swarm Optimization)

Anna University

1. Rajkumar P. Application of particle swarm optimization technique for reactive power optimization problem;.

Degree: Particle swarm optimization technique for reactive power optimization problem, 2013, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/10309

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Reactive power optimization is an important function both in planning and day-to-day operation of power systems. Reactive power planning utilizes the existing reactive power sources… (more)

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Reactive power optimization is an important function both in planning and day-to-day operation of power systems. Reactive power planning utilizes the existing reactive power sources judiciously while planning suitable location and size of VAR compensation to be installed in a system to meet the requirements of future load increase to keep reasonable voltage profile under normal condition and contingency states. RPP considers minimization of the allocation and operational cost as objectives while satisfying the constraints that define satisfactory operation of the system. These objectives can be achieved by proper adjustment of reactive power control variables, such as reactive power outputs of generators, tap ratios of transformers and outputs of shunt capacitors/reactors. Evolutionary computation techniques such as genetic algorithm (GA), Evolutionary Programming (EP), and Evolutionary Strategy (ES) have been proposed to solve the reactive power optimization problem. This thesis proposes Particle Swarm Optimization (PSO) one of the recently introduced population based optimization technique to solve the reactive power planning problem. In this work, the voltage stability level of the system is included as an additional objective of the RPP problem and this multi objective reactive power planning problem is solved using vector evaluated particle swarm optimization (VEPSO) technique. VEPSO is a multi objective variant of particle swarm optimization. In VEPSO, the population is divided into number of sub populations based on the number of objective functions. This thesis proposes a hybrid particle swarm optimization algorithm for reactive power optimization in electricity market. In the proposed HPSO, to avoid the possibility of trapping in local optimum, genetic operators namely crossover and mutation are applied to the PSO algorithm. Hybrid particle swarm algorithms gives better results than the simple genetic algorithm and simple particle swarm optimization algorithm when applying to reactive power optimization

Appendices 1 to 6; pp. 98-136

Advisors/Committee Members: Devaraj, D..

Subjects/Keywords: Evolutionary computation techniques; Genetic Algorthim; evolutionary programming; particle swarm optimization; vector evaluated particle swarm optimization technique

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APA (6^th Edition):

P, R. (2013). Application of particle swarm optimization technique for reactive power optimization problem;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/10309

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):

P, Rajkumar. “Application of particle swarm optimization technique for reactive power optimization problem;.” 2013. Thesis, Anna University. Accessed March 07, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/10309.

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

MLA Handbook (7^th Edition):

P, Rajkumar. “Application of particle swarm optimization technique for reactive power optimization problem;.” 2013. Web. 07 Mar 2021.

Vancouver:

P R. Application of particle swarm optimization technique for reactive power optimization problem;. [Internet] [Thesis]. Anna University; 2013. [cited 2021 Mar 07]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/10309.

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

Council of Science Editors:

P R. Application of particle swarm optimization technique for reactive power optimization problem;. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/10309

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

University of Pretoria

2. Helbig, Marde. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation.

Degree: Computer Science, 2012, University of Pretoria

URL: http://hdl.handle.net/2263/28161

► Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with… (more)

▼ Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms. Advisors/Committee Members: Engelbrecht, Andries P. (advisor).

Subjects/Keywords: Management of boundary constraint violations; Performance measures; Guide updates; Benchmark functions; Dynamic multi-objective optimisation; Particle swarm optimization (PSO); Vector evaluated particle swarm optimisation; UCTD

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

APA (6^th Edition):

Helbig, M. (2012). Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. (Doctoral Dissertation). University of Pretoria. Retrieved from http://hdl.handle.net/2263/28161

Chicago Manual of Style (16^th Edition):

Helbig, Marde. “Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation.” 2012. Doctoral Dissertation, University of Pretoria. Accessed March 07, 2021. http://hdl.handle.net/2263/28161.

MLA Handbook (7^th Edition):

Helbig, Marde. “Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation.” 2012. Web. 07 Mar 2021.

Vancouver:

Helbig M. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. [Internet] [Doctoral dissertation]. University of Pretoria; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2263/28161.

Council of Science Editors:

Helbig M. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. [Doctoral Dissertation]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/28161

University of Pretoria

3. Scheepers, Christiaan. Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer.

Degree: PhD, Computer Science, 2017, University of Pretoria

URL: http://hdl.handle.net/2263/64041

► An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and… (more)

Subjects/Keywords: Multi-objective optimization; Multi-guided particle swarm optimization; Performance measures; Attainment surface; Particle swarm optimization (PSO); Vector evaluated particle swarm optimizer; Porcupine measure; Particle swarm visualization; UCTD

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

APA (6^th Edition):

Scheepers, C. (2017). Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer. (Doctoral Dissertation). University of Pretoria. Retrieved from http://hdl.handle.net/2263/64041

Chicago Manual of Style (16^th Edition):

Scheepers, Christiaan. “Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer.” 2017. Doctoral Dissertation, University of Pretoria. Accessed March 07, 2021. http://hdl.handle.net/2263/64041.

MLA Handbook (7^th Edition):

Scheepers, Christiaan. “Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer.” 2017. Web. 07 Mar 2021.

Vancouver:

Scheepers C. Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer. [Internet] [Doctoral dissertation]. University of Pretoria; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2263/64041.

Council of Science Editors:

Scheepers C. Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer. [Doctoral Dissertation]. University of Pretoria; 2017. Available from: http://hdl.handle.net/2263/64041

University of Pretoria

4. [No author]. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation .

Degree: 2012, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-09242012-211127/

► Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with… (more)

▼ Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms. Advisors/Committee Members: Prof S P Engelbrecht (advisor).

Subjects/Keywords: Management of boundary constraint violations; Performance measures; Guide updates; Benchmark functions; Dynamic multi-objective optimisation; Particle swarm optimisation; Vector evaluated particle swarm optimisation; UCTD

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

APA (6^th Edition):

author], [. (2012). Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation . (Doctoral Dissertation). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-09242012-211127/

Chicago Manual of Style (16^th Edition):

author], [No. “Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation .” 2012. Doctoral Dissertation, University of Pretoria. Accessed March 07, 2021. http://upetd.up.ac.za/thesis/available/etd-09242012-211127/.

MLA Handbook (7^th Edition):

author], [No. “Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation .” 2012. Web. 07 Mar 2021.

Vancouver:

author] [. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation . [Internet] [Doctoral dissertation]. University of Pretoria; 2012. [cited 2021 Mar 07]. Available from: http://upetd.up.ac.za/thesis/available/etd-09242012-211127/.

Council of Science Editors:

author] [. Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation . [Doctoral Dissertation]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-09242012-211127/

5. 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 March 07, 2021. 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. 07 Mar 2021.

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 2021 Mar 07]. 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

NSYSU

6. Li, Pei-yun. Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting.

Degree: Master, Electrical Engineering, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0617117-160757

► Wind power is a key development for the purpose of the global sustainability trends in renewable energy. However, influenced by factors of geography, circumstance and… (more)

Subjects/Keywords: Wind Power Forecasting; Wind Prediction; Particle Swarm Optimization; Support Vector Machine; Clustering Analysis

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APA (6^th Edition):

Li, P. (2017). Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0617117-160757

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):

Li, Pei-yun. “Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting.” 2017. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0617117-160757.

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

MLA Handbook (7^th Edition):

Li, Pei-yun. “Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting.” 2017. Web. 07 Mar 2021.

Vancouver:

Li P. Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0617117-160757.

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

Council of Science Editors:

Li P. Applying K-means Clustering Algorithm and MSVM for Wind Power Forecasting. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0617117-160757

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

Iowa State University

7. Bertram, Aaron M. Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm.

Degree: 2019, Iowa State University

URL: https://lib.dr.iastate.edu/etd/17402

► A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is… (more)

Subjects/Keywords: Compression Igntion; Machine Learning; Optimization; Particle Swarm; Support Vector Regression; Mechanical Engineering

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

APA (6^th Edition):

Bertram, A. M. (2019). Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17402

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):

Bertram, Aaron M. “Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm.” 2019. Thesis, Iowa State University. Accessed March 07, 2021. https://lib.dr.iastate.edu/etd/17402.

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

MLA Handbook (7^th Edition):

Bertram, Aaron M. “Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm.” 2019. Web. 07 Mar 2021.

Vancouver:

Bertram AM. Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm. [Internet] [Thesis]. Iowa State University; 2019. [cited 2021 Mar 07]. Available from: https://lib.dr.iastate.edu/etd/17402.

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

Council of Science Editors:

Bertram AM. Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm. [Thesis]. Iowa State University; 2019. Available from: https://lib.dr.iastate.edu/etd/17402

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

Indian Institute of Science

8. Naik, G Narayana. Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion.

Degree: PhD, Faculty of Enginering, 2009, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/469

► In recent years, use of composites is increasing in most fields of engineering such as aerospace, automotive, civil construction, marine, prosthetics, etc., because of its… (more)

▼ In recent years, use of composites is increasing in most fields of engineering such as aerospace, automotive, civil construction, marine, prosthetics, etc., because of its light weight, very high specific strength and stiffness, corrosion resistance, high thermal resistance etc. It can be seen that the specific strength of fibers are many orders more compared to metals. Thus, laminated fiber reinforced plastics have emerged to be attractive materials for many engineering applications. Though the uses of composites are enormous, there is always an element of fuzziness in the design of composites. Composite structures are required to be designed to resist high stresses. For this, one requires a reliable failure criterion. The anisotropic behaviour of composites makes it very difficult to formulate failure criteria and experimentally verify it, which require one to perform necessary bi-axial tests and plot the failure envelopes. Failure criteria are usually based on certain assumption, which are some times questionable. This is because, the failure process in composites is quite complex. The failure in a composite is normally based on initiating failure mechanisms such as fiber breaks, fiber compressive failure, matrix cracks, matrix crushing, delamination, disbonds or a combination of these. The initiating failure mechanism is the one, which is/are responsible for initiating failure in a laminated composites. Initiating failure mechanisms are generally dependant on the type of loading, geometry, material properties, condition of manufacture, boundary conditions, weather conditions etc. Since, composite materials exhibit directional properties, their applications and failure conditions should be properly examined and in addition to this, robust computational tools have to be exploited for the design of structural components for efficient utilisation of these materials. Design of structural components requires reliable failure criteria for the safe design of the components. Several failure criteria are available for the design of composite laminates. None of the available anisotropic strength criteria represents observed results sufficiently accurate to be employed confidently by itself in design. Most of the failure criteria are validated based on the available uniaxial test data, whereas, in practical situations, laminates are subjected to at least biaxial states of stresses. Since, the generation of biaxial test data are very difficult and time consuming to obtain, it is indeed a necessity to develop computational tools for modelling the biaxial behavior of the composite laminates. Understanding of the initiating failure mechanisms and the development of reliable failure criteria is an essential prerequisite for effective utilization of composite materials. Most of the failure criteria, considers the uniaxial test data with constant shear stress to develop failure envelopes, but in reality, structures are subjected to biaxial normal stresses as well as shear stresses. Hence, one can develop different failure… Advisors/Committee Members: Krishna Murty, A V (advisor).

Subjects/Keywords: Structural Analysis; Laminated Composites; Composites - Design Optimization; Failure Mechanism Based Failure Criterion; Composite Laminates - Failure Mechanism Based Design; Vector Evaluated Particle Swarm Optimization; Genetic Algorithms; Composite Laminates; Composite Laminae; Composite Structures; Design Optimization; Failure Criterion; Structural Engineering

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

APA (6^th Edition):

Naik, G. N. (2009). Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/469

Chicago Manual of Style (16^th Edition):

Naik, G Narayana. “Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion.” 2009. Doctoral Dissertation, Indian Institute of Science. Accessed March 07, 2021. http://etd.iisc.ac.in/handle/2005/469.

MLA Handbook (7^th Edition):

Naik, G Narayana. “Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion.” 2009. Web. 07 Mar 2021.

Vancouver:

Naik GN. Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2009. [cited 2021 Mar 07]. Available from: http://etd.iisc.ac.in/handle/2005/469.

Council of Science Editors:

Naik GN. Development And Design Optimization Of Laminated Composite Structures Using Failure Mechanism Based Failure Criterion. [Doctoral Dissertation]. Indian Institute of Science; 2009. Available from: http://etd.iisc.ac.in/handle/2005/469

University of Pretoria

9. 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 March 07, 2021. http://hdl.handle.net/2263/33333.

MLA Handbook (7^th Edition):

Cleghorn, Christopher Wesley. “A Generalized theoretical deterministic particle swarm model.” 2013. Web. 07 Mar 2021.

Vancouver:

Cleghorn CW. A Generalized theoretical deterministic particle swarm model. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2021 Mar 07]. 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

10. 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 (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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Ali MY. Energy management system of a microgrid with distributed generation. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2021 Mar 07]. 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

11. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Drosos C. Optimized heuristic implementations in al multi - agent systems. [Internet] [Thesis]. University of Piraeus (UNIPI); Πανεπιστήμιο Πειραιώς; 2016. [cited 2021 Mar 07]. 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

Texas A&M University

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

Degree: MS, Nuclear Engineering, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Lakshmipathy, Sathish Kumar. “VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D.” 2012. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188.

MLA Handbook (7^th Edition):

Lakshmipathy, Sathish Kumar. “VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D.” 2012. Web. 07 Mar 2021.

Vancouver:

Lakshmipathy SK. VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D. [Internet] [Masters thesis]. Texas A&M University; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11188.

Council of Science Editors:

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

NSYSU

13. Lin, Chien-hung. TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems.

Degree: Master, Electrical Engineering, 2008, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-145929

► One of the major problems encountered in wireless location is the effect caused by non-line of sight (NLOS) propagation. When the direct path from the… (more)

Subjects/Keywords: TOA; particle swarm optimization (PSO); wireless location; UWB; NLOS; least squares-support vector machine (LS-SVM)

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APA (6^th Edition):

Lin, C. (2008). TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-145929

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):

Lin, Chien-hung. “TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems.” 2008. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-145929.

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

MLA Handbook (7^th Edition):

Lin, Chien-hung. “TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems.” 2008. Web. 07 Mar 2021.

Vancouver:

Lin C. TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-145929.

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

Council of Science Editors:

Lin C. TOA Wireless Location Algorithm with NLOS Mitigation Based on LS-SVM in UWB Systems. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-145929

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

NSYSU

14. Chen, Chun-hao. An Enhanced Initialization Method for Codebook Generation.

Degree: Master, Computer Science and Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0806115-161941

► Linde-Buzo-Gray algorithm (LBG) is one of the most well-known image compressionalgorithms for the codebook generation problem of vector quantization. But in recent years,metaheuristic algorithms have… (more)

Subjects/Keywords: simulated annealing; codebook generation problem; particle swarm optimization (PSO); Vector quantization; tabu search; Linde-Buzo-Gray (LBG)

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APA (6^th Edition):

Chen, C. (2015). An Enhanced Initialization Method for Codebook Generation. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0806115-161941

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):

Chen, Chun-hao. “An Enhanced Initialization Method for Codebook Generation.” 2015. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0806115-161941.

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

MLA Handbook (7^th Edition):

Chen, Chun-hao. “An Enhanced Initialization Method for Codebook Generation.” 2015. Web. 07 Mar 2021.

Vancouver:

Chen C. An Enhanced Initialization Method for Codebook Generation. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0806115-161941.

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

Council of Science Editors:

Chen C. An Enhanced Initialization Method for Codebook Generation. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0806115-161941

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

Penn State University

15. So, Jinhyun. Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification.

Degree: 2013, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/18879

► Infinite impulse response (IIR) systems are widely used in modeling systems such as communications, bio-systems, acoustics, etc. Many algorithms in computational intelligence area have been… (more)

Subjects/Keywords: IIR system; cat swarm optimization; particle swarm optimization

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APA (6^th Edition):

So, J. (2013). Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/18879

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):

So, Jinhyun. “Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification.” 2013. Thesis, Penn State University. Accessed March 07, 2021. https://submit-etda.libraries.psu.edu/catalog/18879.

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

MLA Handbook (7^th Edition):

So, Jinhyun. “Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification.” 2013. Web. 07 Mar 2021.

Vancouver:

So J. Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification. [Internet] [Thesis]. Penn State University; 2013. [cited 2021 Mar 07]. Available from: https://submit-etda.libraries.psu.edu/catalog/18879.

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

Council of Science Editors:

So J. Comparison of cat swarm optimization and particle swarm optimization algorithm for Iir system identification. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/18879

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

16. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

M S. A study on process characteristics of CNC end milling process For al7075 t6 aluminium alloy;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Mar 07]. 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

Universidad de Chile

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

Degree: 2012, Universidad de Chile

URL: http://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.

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

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

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://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 March 07, 2021. http://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. 07 Mar 2021.

Vancouver:

Lai C. Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO. [Internet] [Thesis]. Universidad de Chile; 2012. [cited 2021 Mar 07]. Available from: http://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://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

Brigham Young University

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

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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

McNabb AW. Parallel Particle Swarm Optimization and Large Swarms. [Internet] [Masters thesis]. Brigham Young University; 2011. [cited 2021 Mar 07]. 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 Manitoba

19. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Franz W. Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition. [Internet] [Masters thesis]. University of Manitoba; 2013. [cited 2021 Mar 07]. 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

20. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Shah RH. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Mar 07]. 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

IUPUI

21. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Chong Chie JAKH. Motion correction of PET/CT images. [Internet] [Thesis]. IUPUI; 2017. [cited 2021 Mar 07]. 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

University of Illinois – Urbana-Champaign

22. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Stanley A. Identifying near-earth asteroid targets for human exploration using particle swarm optimization. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2013. [cited 2021 Mar 07]. 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

23. 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 March 07, 2021. 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. 07 Mar 2021.

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 2021 Mar 07]. 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.

University of Waterloo

24. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

El-Abd M. Cooperative Models of Particle Swarm Optimizers. [Internet] [Thesis]. University of Waterloo; 2008. [cited 2021 Mar 07]. 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

Anna University

25. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

D Jm. Certain optimization techniques for Floorplanning in vlsi physical design;. [Internet] [Thesis]. Anna University; 2014. [cited 2021 Mar 07]. 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

26. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

G K. Certain investigations on data Clustering using hybrid algorithms for Unlabeled data sets;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Mar 07]. 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

University of Georgia

27. Smythe, Jared Michael Dean. Roulette Wheel Particle Swarm Optimization.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/28119

► In this paper a new probability-based multi-valued Particle Swarm Optimization algorithm is developed for problems with nominal variables. The algorithm more explicitly contextualizes probability updates… (more)

Subjects/Keywords: Multi-valued particle swarm optimization; Discrete particle swarm optimization; Forest planning; Raindrop optimization; Nominal variables; Combinatorial optimization; Probability optimization

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APA (6^th Edition):

Smythe, J. M. D. (2014). Roulette Wheel Particle Swarm Optimization. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/28119

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):

Smythe, Jared Michael Dean. “Roulette Wheel Particle Swarm Optimization.” 2014. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/28119.

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

MLA Handbook (7^th Edition):

Smythe, Jared Michael Dean. “Roulette Wheel Particle Swarm Optimization.” 2014. Web. 07 Mar 2021.

Vancouver:

Smythe JMD. Roulette Wheel Particle Swarm Optimization. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/28119.

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

Council of Science Editors:

Smythe JMD. Roulette Wheel Particle Swarm Optimization. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/28119

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

NSYSU

28. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Mar 07]. 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

29. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2021 Mar 07]. 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

30. 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 March 07, 2021. 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. 07 Mar 2021.

Vancouver:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2021 Mar 07]. 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

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