A Modified Differential Evolution Algorithm with Activation Strategy
碩士 === 中原大學 === 資訊管理研究所 === 97 === Evolutionary Computation (EC) provides high performance on real world optimization problems such as scheduling, resource distribution, portfolio optimization etc. Differential Evolution (DE) algorithm was first reported in 1996. Based on the characteristics of simp...
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ndltd-TW-097CYCU53960222015-10-13T12:04:43Z http://ndltd.ncl.edu.tw/handle/24511459914337920395 A Modified Differential Evolution Algorithm with Activation Strategy 運用活化策略改良差分演化演算法 Zhan-Rong Hsu 許展榮 碩士 中原大學 資訊管理研究所 97 Evolutionary Computation (EC) provides high performance on real world optimization problems such as scheduling, resource distribution, portfolio optimization etc. Differential Evolution (DE) algorithm was first reported in 1996. Based on the characteristics of simple structure, high accuracy and efficiency, and the requirement of fewer parameters, DE has received significant attention from researchers. It has been applied to numerous fields and performs much better than other evolutionary computation. Although DE represents powerful performance, but it has attach importance to the drawbacks of unstable convergence, breakaway the solution space, and the common defect of evolution computation “dropping into regional optimum”. In this study, we attempt to improve the traditional differential evolution algorithm and propose a novel algorithm “Activated Strategy Differential Evolution” (ASDE). Based on import the Activated Strategy (AS) intensified the structure of traditional DE for balancing the solution accuracy and stability. Wei-Ping Lee 李維平 2009 學位論文 ; thesis 75 zh-TW |
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碩士 === 中原大學 === 資訊管理研究所 === 97 === Evolutionary Computation (EC) provides high performance on real world optimization problems such as scheduling, resource distribution, portfolio optimization etc. Differential Evolution (DE) algorithm was first reported in 1996. Based on the characteristics of simple structure, high accuracy and efficiency, and the requirement of fewer parameters, DE has received significant attention from researchers. It has been applied to numerous fields and performs much better than other evolutionary computation.
Although DE represents powerful performance, but it has attach importance to the drawbacks of unstable convergence, breakaway the solution space, and the common defect of evolution computation “dropping into regional optimum”. In this study, we attempt to improve the traditional differential evolution algorithm and propose a novel algorithm “Activated Strategy Differential Evolution” (ASDE). Based on import the Activated Strategy (AS) intensified the structure of traditional DE for balancing the solution accuracy and stability.
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Wei-Ping Lee |
author_facet |
Wei-Ping Lee Zhan-Rong Hsu 許展榮 |
author |
Zhan-Rong Hsu 許展榮 |
spellingShingle |
Zhan-Rong Hsu 許展榮 A Modified Differential Evolution Algorithm with Activation Strategy |
author_sort |
Zhan-Rong Hsu |
title |
A Modified Differential Evolution Algorithm with Activation Strategy |
title_short |
A Modified Differential Evolution Algorithm with Activation Strategy |
title_full |
A Modified Differential Evolution Algorithm with Activation Strategy |
title_fullStr |
A Modified Differential Evolution Algorithm with Activation Strategy |
title_full_unstemmed |
A Modified Differential Evolution Algorithm with Activation Strategy |
title_sort |
modified differential evolution algorithm with activation strategy |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/24511459914337920395 |
work_keys_str_mv |
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