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You searched for +publisher:"North Carolina State University" +contributor:("Dr. Salah Elmaghraby, Committee Member"). Showing records 1 – 2 of 2 total matches.

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North Carolina State University

1. Radhakrishnan, Alamelu. Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization.

Degree: MS, Operations Research, 2007, North Carolina State University

Multiobjective optimization (MO) is the problem of maximizing⁄minimizing a set of nonlinear objective functions (modeling several performance criteria) subject to a set of nonlinear constraints(modeling availability of resources).The MO problem has several applications in science, engineering, finance, etc. It is normally not possible to find an optimal solution in MO, since the various objective functions in the problem are usually in conflict with each other. Therefore, the objective in MO is to find the "Pareto front" of efficient solutions that provide a tradeoff between the various objectives.Classical techniques assign weights to the various objectives in the MO problem, and solve the resulting single objective problem using standard algorithms for nonlinear optimization. Moreover, these techniques only compute a single solution to the problem forcing the decision maker to miss out on other desirable solutions in the MO problem. We investigate the use of evolutionary algorithms to solve MO problems in this thesis. Unlike classical methods, evolutionary strategies directly solve the MO problem to find the Pareto front. These algorithms use probabilistic rules to search for solutions and are very efficient in solving medium sized MO problems. We use evolutionary algorithms to compute the "efficient frontier" in the classical Markowitz mean-variance optimization problem in finance, and illustrate our results on an example. Advisors/Committee Members: Dr. Jeffrey Scroggs, Committee Member (advisor), Dr. Salah Elmaghraby, Committee Member (advisor), Dr. Negash Medhin, Committee Chair (advisor).

Subjects/Keywords: portfolio optimization; multiobjective optimzation; differential evolution; evolutionary algorithms

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

APA (6th Edition):

Radhakrishnan, A. (2007). Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization. (Thesis). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/1386

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

Chicago Manual of Style (16th Edition):

Radhakrishnan, Alamelu. “Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization.” 2007. Thesis, North Carolina State University. Accessed October 20, 2020. http://www.lib.ncsu.edu/resolver/1840.16/1386.

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

MLA Handbook (7th Edition):

Radhakrishnan, Alamelu. “Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization.” 2007. Web. 20 Oct 2020.

Vancouver:

Radhakrishnan A. Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization. [Internet] [Thesis]. North Carolina State University; 2007. [cited 2020 Oct 20]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/1386.

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

Council of Science Editors:

Radhakrishnan A. Evolutionary Algorithms for Multiobjective Optimization with Applications in Portfolio Optimization. [Thesis]. North Carolina State University; 2007. Available from: http://www.lib.ncsu.edu/resolver/1840.16/1386

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


North Carolina State University

2. Lea, Djuana. Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks.

Degree: PhD, Operations Research, 2005, North Carolina State University

The routing and wavelength assignment (RWA) problem is essential for achieving efficient performance in wavelength-routed optical networks. For a network without wavelength conversion capabilities, the RWA problem consists of selecting an appropriate path and wavelength for each connection request while ensuring that paths that share common links are not assigned the same wavelength. The purpose of this research is to develop efficient adaptive methods for routing and wavelength assignment in wavelength-routed optical networks with dynamic traffic. The proposed methods utilize soft computing techniques including genetic algorithms, fuzzy control theory, simulated annealing, and tabu search. All four algorithms consider the current availability of network resources before making a routing decision. Simulations for each algorithm show that each method outperforms fixed and alternate routing strategies. The fuzzy-controlled algorithm achieved the lowest blocking rates and the shortest running times in most cases. Advisors/Committee Members: Dr. Henry Nuttle, Committee Member (advisor), Dr. Elmor Peterson, Committee Member (advisor), Dr. Salah Elmaghraby, Committee Member (advisor), Dr. Shu-Cherng Fang, Committee Chair (advisor).

Subjects/Keywords: wavelength division multiplexing

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

APA (6th Edition):

Lea, D. (2005). Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5955

Chicago Manual of Style (16th Edition):

Lea, Djuana. “Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks.” 2005. Doctoral Dissertation, North Carolina State University. Accessed October 20, 2020. http://www.lib.ncsu.edu/resolver/1840.16/5955.

MLA Handbook (7th Edition):

Lea, Djuana. “Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks.” 2005. Web. 20 Oct 2020.

Vancouver:

Lea D. Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks. [Internet] [Doctoral dissertation]. North Carolina State University; 2005. [cited 2020 Oct 20]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5955.

Council of Science Editors:

Lea D. Soft Computing Approaches to Routing and Wavelength Assignment in Wavelength-Routed Optical Networks. [Doctoral Dissertation]. North Carolina State University; 2005. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5955

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