New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.

New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.

An efficient technique in handling a large number of constraints is through the evolutionary computation (EC) method such as a Genetic Algorithms(GAs0, Evolutionary Programming(EP), Evolutionary Strategies(ES) etc. Particle Swarm Optimization is a population based optimization technique under EC cat...

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Main Author: Ting , Tiew On
Format: Thesis
Published: 2004
Subjects:
QA76.75-76.765 Computer software
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id my-mmu-ep.791
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spelling my-mmu-ep.7912010-07-02T04:20:32Z New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem. 2004-08 Ting , Tiew On QA76.75-76.765 Computer software An efficient technique in handling a large number of constraints is through the evolutionary computation (EC) method such as a Genetic Algorithms(GAs0, Evolutionary Programming(EP), Evolutionary Strategies(ES) etc. Particle Swarm Optimization is a population based optimization technique under EC category. In this study, the performance of the Particle Swarm Optimization (PSO) algorithm is improved considerably by introducing a new class of operators to manipulate particles in each generation. These operators are chosen from an empirical study and testing of a large number of opeators. From this empirical study, it is found that the performance of the operators differs from each other and varies with the benchmark problems. Among these operators, the best operators are chosen and divided into three categories namely mutation, crossover and variant. 2004-08 Thesis http://shdl.mmu.edu.my/791/ http://myto.perpun.net.my/metoalogin/logina.php masters Multimedia University Research Library
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Ting , Tiew On
New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
description An efficient technique in handling a large number of constraints is through the evolutionary computation (EC) method such as a Genetic Algorithms(GAs0, Evolutionary Programming(EP), Evolutionary Strategies(ES) etc. Particle Swarm Optimization is a population based optimization technique under EC category. In this study, the performance of the Particle Swarm Optimization (PSO) algorithm is improved considerably by introducing a new class of operators to manipulate particles in each generation. These operators are chosen from an empirical study and testing of a large number of opeators. From this empirical study, it is found that the performance of the operators differs from each other and varies with the benchmark problems. Among these operators, the best operators are chosen and divided into three categories namely mutation, crossover and variant.
format Thesis
qualification_level Master's degree
author Ting , Tiew On
author_facet Ting , Tiew On
author_sort Ting , Tiew On
title New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
title_short New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
title_full New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
title_fullStr New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
title_full_unstemmed New Class Of Operations To Accelerate Particle Swarm Optimization Algorithm And A Novel Hybrid Approach For Unit Commitment Problem.
title_sort new class of operations to accelerate particle swarm optimization algorithm and a novel hybrid approach for unit commitment problem.
granting_institution Multimedia University
granting_department Research Library
publishDate 2004
_version_ 1747829217242906624

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