记忆增强的动态多目标分解进化算法  

Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition

作　　者：刘敏[1,2,3] 曾文华[3,4] 

机构地区：[1]漳州师范学院计算机科学与工程系,福建漳州363000 [2]厦门大学智能科学与技术系,福建厦门361005 [3]福建省仿脑智能系统重点实验室(厦门大学),福建厦门361005 [4]厦门大学软件学院,福建厦门361005

出　　处：《软件学报》2013年第7期1571-1588,共18页Journal of Software

基　　金：国家自然科学基金(60975076);福建省教育厅科技项目(JA12221)

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.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.

关 键 词：进化计算 多目标优化 动态环境 记忆方法 分解 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]