Modified Differential Evolution with Locality induced Genetic Operators for dynamic optimization
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- 作者：Rohan Mukherjeea ; rm38@rice.edu" class="auth_mail" title="E-mail the corresponding author ; Shantanab Debchoudhuryb ; sdch10@vt.edu" class="auth_mail" title="E-mail the corresponding author ; Swagatam Dasc ; swagatamdas19@yahoo.co.in" class="auth_mail" title="E-mail the corresponding author ; swagatam.das@isical.ac.in" class="auth_mail" title="E-mail the corresponding author
- 关键词：Continuous optimization ; Dynamic optimization ; Differential Evolution ; Self adaptation ; Genetic operators
- 刊名：European Journal of Operational Research
- 出版年：2016
- 出版时间：1 September 2016
- 年：2016
- 卷：253
- 期：2
- 页码：337-355
- 全文大小：3086 K
文摘
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We present a modified Differential Evolution (DE) for dynamic optimization.
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The modified DE mutation can retain the proximity information for each solution.
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A local-best crossover operation helps in preserving diversity.
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We propose an exhaustive dynamic change detection technique.
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Our algorithm has been extensively tested and validated w.r.t. the state-of-the-art.