| 动态环境下基于可变记忆的进化算法 |
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| 引用本文： | 关守平,尹晓峰.动态环境下基于可变记忆的进化算法[J].东北大学学报(自然科学版),2011,32(6):777-780. |
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| 作者姓名： | 关守平 尹晓峰 |
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| 作者单位： | 东北大学信息科学与工程学院,辽宁沈阳,110819 |
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| 基金项目： | 国家自然科学基金资助项目(60974070) |
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| 摘 要： | 常规基于记忆的进化算法在动态环境中往往达不到期望的效果,这主要是由于记忆体大小的限制.为此提出了动态环境下基于可变记忆的进化算法(IMEEA),其核心思想是算法中拥有两个种群,即搜索种群和记忆种群,同时采用过度变异策略来增加种群的多样性.算法中的两个种群有最小和最大的允许长度,并且种群的大小根据进化过程的进行而不断变化...
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| 关 键 词： | 动态优化 进化算法 记忆 多样性 |
The Variable Size Memory-based Evolutionary Algorithm in Dynamic Environments |
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| GUAN Shou-ping,YIN Xiao-feng.The Variable Size Memory-based Evolutionary Algorithm in Dynamic Environments[J].Journal of Northeastern University(Natural Science),2011,32(6):777-780. |
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| Authors: | GUAN Shou-ping YIN Xiao-feng |
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| Institution: | GUAN Shou-ping,YIN Xiao-feng(School of Information Science & Engineering,Northeastern University,Shenyang 110819,China.) |
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| Abstract: | Traditional memory-based evolutionary algorithms often may not achieve the desired performances in dynamic environments,which is mainly due to the fixed memory size.A variable size memory-based evolutionary algorithm is proposed.The improved memory enhanced evolutionary algorithm(IMEEA),which combines memory population and search population,and hyper-mutation is used to promote and maintain diversity.The two populations have minimum and maximum sizes allowed that change according to the stage of the evoluti... |
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| Keywords: | dynamic optimization evolutionary algorithm memory diversity |
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