Strengthen-reverse and reduction for particle swarm optimization

碩士 === 國立中央大學 === 電機工程學系 === 101 === This study aims to explore the problems of strengthen-reverse and reduction for particle swarm optimization. Genetic algorithm, particle swarm optimization and simulated annealing are widely used to search for the global optimal solutions of fitness functions. Th...

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Main Authors: Shih-Hua Wang, 王士驊
Other Authors: Hung-Yuan Chung
Format: Others
Language:zh-TW
Published: 2013
Online Access:http://ndltd.ncl.edu.tw/handle/86154232760082899113
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spelling ndltd-TW-101NCU054421462015-10-13T22:34:51Z http://ndltd.ncl.edu.tw/handle/86154232760082899113 Strengthen-reverse and reduction for particle swarm optimization 強化反向減量粒子群最佳化演算法 Shih-Hua Wang 王士驊 碩士 國立中央大學 電機工程學系 101 This study aims to explore the problems of strengthen-reverse and reduction for particle swarm optimization. Genetic algorithm, particle swarm optimization and simulated annealing are widely used to search for the global optimal solutions of fitness functions. The present work tries to make some improvements and to reduce the consuming time of the generalized optimization algorithms. Whether the generalized optimization algorithms are good or bad usually depends on the fitness function value. This paper tried to use high pointing-behavior to make the speed of seeking out the global optimum being higher. But we need to increase the chance of escaping from the local optimal solutions. Final the new algorithms are as simplified as possible and that the user will apply these algorithms more easy than others. In addition, the simulation is given to verify the feasibility of the present method. Hung-Yuan Chung 鍾鴻源 2013 學位論文 ; thesis 66 zh-TW
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language zh-TW
format Others
sources NDLTD
description 碩士 === 國立中央大學 === 電機工程學系 === 101 === This study aims to explore the problems of strengthen-reverse and reduction for particle swarm optimization. Genetic algorithm, particle swarm optimization and simulated annealing are widely used to search for the global optimal solutions of fitness functions. The present work tries to make some improvements and to reduce the consuming time of the generalized optimization algorithms. Whether the generalized optimization algorithms are good or bad usually depends on the fitness function value. This paper tried to use high pointing-behavior to make the speed of seeking out the global optimum being higher. But we need to increase the chance of escaping from the local optimal solutions. Final the new algorithms are as simplified as possible and that the user will apply these algorithms more easy than others. In addition, the simulation is given to verify the feasibility of the present method.
author2 Hung-Yuan Chung
author_facet Hung-Yuan Chung
Shih-Hua Wang
王士驊
author Shih-Hua Wang
王士驊
spellingShingle Shih-Hua Wang
王士驊
Strengthen-reverse and reduction for particle swarm optimization
author_sort Shih-Hua Wang
title Strengthen-reverse and reduction for particle swarm optimization
title_short Strengthen-reverse and reduction for particle swarm optimization
title_full Strengthen-reverse and reduction for particle swarm optimization
title_fullStr Strengthen-reverse and reduction for particle swarm optimization
title_full_unstemmed Strengthen-reverse and reduction for particle swarm optimization
title_sort strengthen-reverse and reduction for particle swarm optimization
publishDate 2013
url http://ndltd.ncl.edu.tw/handle/86154232760082899113
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