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Journal of Software:2013.24(7):1571-1588

记忆增强的动态多目标分解进化算法
刘敏,曾文华
(漳州师范学院 计算机科学与工程系, 福建 漳州 363000;厦门大学 智能科学与技术系, 福建 厦门 361005;福建省仿脑智能系统重点实验室(厦门大学), 福建 厦门 361005;福建省仿脑智能系统重点实验室(厦门大学), 福建 厦门 361005;厦门大学 软件学院, 福建 厦门 361005)
Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition
LIU Min,ZENG Wen-Hua
(Department of Computer Science and Engineering, Zhangzhou Normal University, Zhangzhou 363000, China;Cognitive Science Department, Xiamen University, Xiamen 361005, China;Fujian Provincial Key Laboratory of Brain-Like Intelligent Systems (Xiamen University), Xiamen 361005, China;Fujian Provincial Key Laboratory of Brain-Like Intelligent Systems (Xiamen University), Xiamen 361005, China;Software School, Xiamen University, Xiamen 361005, China)
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Received:December 27, 2011    Revised:July 26, 2012
> 中文摘要: 现实世界中的一些多目标优化问题经常受动态环境影响而不断发生变化,要求优化算法不断地及时跟踪时变的Pareto 最优解集.提出了一种记忆增强的动态多目标分解进化算法.将动态多目标优化问题分解为若干个动态单目标优化子问题并同时优化这些子问题,以便快速逼近Pareto 最优解集.给出了一个改进的环境变化检测算子,以便更好地检测环境变化.设计了一种基于子问题的串式记忆方法,利用过去类似环境下搜索到的最优解来有效地响应新的环境变化.在8 个标准的测试问题上,将新算法与其他3 种记忆增强的动态进化多目标优化算法进行了实验比较.结果表明,新算法比其他3 种算法具有更快的运行速度、更强的记忆能力与鲁棒性能,并且新算法所获得的解集还具有更好的收敛性与分布性.
Abstract:In addition to the need for satisfying several objectives, many real-world problems are also dynamic and require the optimization algorithm to continuously track the time-varying Pareto optimal set over time. This paper proposes a memory enhanced dynamic multi-objective evolutionary algorithm based on decomposition (denoted by dMOEAD-M). Specifically, the dMOEAD-M decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and optimizes them simultaneously. An improved environment detection operator is presented. Also, a subproblem-based bunchy memory scheme, which allows evolutionary algorithm to store good solutions from old environments and reuse them as necessary, is designed to respond to the environment change. Simulation results on eight benchmark problems show that the proposed dMOEAD-M not only runs at a faster speed, more memory capabilities, and a better robustness, but is also able to find a much better spread of solutions and converge better near the changing Pareto optimal front, compared with three other memory enhanced dynamic evolutionary multi-objective optimization algorithms.
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基金项目:国家自然科学基金(60975076); 福建省教育厅科技项目(JA12221) 国家自然科学基金(60975076); 福建省教育厅科技项目(JA12221)
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刘敏,曾文华.记忆增强的动态多目标分解进化算法.软件学报,2013,24(7):1571-1588

LIU Min,ZENG Wen-Hua.Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition.Journal of Software,2013,24(7):1571-1588