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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Main Authors: Zhan-Rong Hsu, 許展榮
Other Authors: Wei-Ping Lee
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/24511459914337920395
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spelling 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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language zh-TW
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description 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.
author2 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
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