An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization
An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization
复制标题
一种基于分解的外部档案引导组合优化多目标进化算法
DOI：
10.1109/tevc.2014.2350995
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发表时间：
2015-08-01
影响因子：
14.3
通讯作者：
Zhang, Qingfu
中科院分区：
文献类型：
--
作者：
Cai, Xinye;Li, Yexing;Zhang, Qingfu
基于支配的排序和分解是多目标进化优化的两种基本策略。本文提出了一种混合多目标进化算法集成这两种不同的策略，为两个或三个目标的组合优化问题。该算法适用于内部（工作）人口和外部档案。它使用基于分解的策略来发展其工作人口，并使用基于支配的排序来维护外部存档。从外部档案中提取的信息用于决定在每一代中应该搜索哪些搜索区域。这样，基于支配的排序和分解策略就可以相辅相成。在我们的实验研究中，所提出的算法进行了比较，基于支配的方法，基于分解的，其增强的变种之一，两个著名的多目标组合优化问题。实验结果表明，我们提出的算法优于其他方法。外部存档在所提出的算法的影响进行了研究和讨论。
Domination-based sorting and decomposition are two basic strategies used in multiobjective evolutionary optimization. This paper proposes a hybrid multiobjective evolutionary algorithm integrating these two different strategies for combinatorial optimization problems with two or three objectives. The proposed algorithm works with an internal (working) population and an external archive. It uses a decomposition-based strategy for evolving its working population and uses a domination-based sorting for maintaining the external archive. Information extracted from the external archive is used to decide which search regions should be searched at each generation. In such a way, the domination-based sorting and the decomposition strategy can complement each other. In our experimental studies, the proposed algorithm is compared with a domination-based approach, a decomposition-based one, and one of its enhanced variants on two well-known multiobjective combinatorial optimization problems. Experimental results show that our proposed algorithm outperforms other approaches. The effects of the external archive in the proposed algorithm are also investigated and discussed.