A Novel Differential Evolution Algorithm with co-evolution strategy
碩士 === 中原大學 === 資訊管理研究所 === 98 === Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent year. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on complex optimization prob...
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ndltd-TW-098CYCU53960192015-10-13T18:44:54Z http://ndltd.ncl.edu.tw/handle/42161994725526399905 A Novel Differential Evolution Algorithm with co-evolution strategy 運用多群協同概念改良差分演化演算法 Wan-Jou Chien 簡宛柔 碩士 中原大學 資訊管理研究所 98 Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent year. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on complex optimization problem. However, there are some problems, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those disadvantages, a different method was proposed, named CO-DE, by combining with a simple co-evolutionary model and reset mechanism. Thus, CO-DE can maintain appropriate swarm diversity and reduce the premature convergence. On the other hand, a reset mechanism was set to avoid the particle stagnates, which can further improve the performance of differential evolution. The proposed model can be now successfully applied with some well-known benchmark functions. Wei-Ping Lee 李維平 2010 學位論文 ; thesis 58 zh-TW |
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碩士 === 中原大學 === 資訊管理研究所 === 98 === Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent year. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on complex optimization problem. However, there are some problems, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those disadvantages, a different method was proposed, named CO-DE, by combining with a simple co-evolutionary model and reset mechanism. Thus, CO-DE can maintain appropriate swarm diversity and reduce the premature convergence. On the other hand, a reset mechanism was set to avoid the particle stagnates, which can further improve the performance of differential evolution. The proposed model can be now successfully applied with some well-known benchmark functions.
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author2 |
Wei-Ping Lee |
author_facet |
Wei-Ping Lee Wan-Jou Chien 簡宛柔 |
author |
Wan-Jou Chien 簡宛柔 |
spellingShingle |
Wan-Jou Chien 簡宛柔 A Novel Differential Evolution Algorithm with co-evolution strategy |
author_sort |
Wan-Jou Chien |
title |
A Novel Differential Evolution Algorithm with co-evolution strategy |
title_short |
A Novel Differential Evolution Algorithm with co-evolution strategy |
title_full |
A Novel Differential Evolution Algorithm with co-evolution strategy |
title_fullStr |
A Novel Differential Evolution Algorithm with co-evolution strategy |
title_full_unstemmed |
A Novel Differential Evolution Algorithm with co-evolution strategy |
title_sort |
novel differential evolution algorithm with co-evolution strategy |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/42161994725526399905 |
work_keys_str_mv |
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