Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization
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DOI：
10.1109/tevc.2014.2350987
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发表时间：
2015-08-01
影响因子：
14.3
通讯作者：
Yao, Xin
中科院分区：
文献类型：
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作者：
Wang, Handing;Jiao, Licheng;Yao, Xin
多目标优化问题通常是指具有三个以上目标的多目标问题。它们的大量目标在收敛、多样性和复杂性方面对多目标进化算法(MOEA)提出了挑战。大多数现有的经济实体只能在这三个方面中的一个方面表现良好。有鉴于此，我们的目标是同时在这三个方面对ManyOps进行更平衡的MOEA设计。在已有的多目标进化算法中，两档案库算法(Two_Arch)是一种低复杂度的算法，具有两个档案库，分别关注收敛和多样性。受Two_Arch思想的启发，本文针对ManyOps提出了一个显著改进的双归档算法(Two_Arch2)。在Two_Arch2中，我们为两个档案分配了不同的选择原则(基于指示器和基于Pareto)。此外，我们在Two_Arch2中设计了一种新的基于LP范数(p<；1)的ManyOps分集维护方案。为了评估Two_Arch2在ManyOps上的性能，我们将其与几个MOEA在不同目标数的基准问题上进行了比较。实验结果表明，Two_Arch2算法能够处理多达20个目标的ManyOps问题，具有良好的收敛、多样性和复杂性。
Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two_Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two_Arch, we propose a significantly improved two-archive algorithm (i.e., Two_Arch2) for ManyOPs in this paper. In our Two_Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based (p < 1) diversity maintenance scheme for ManyOPs in Two_Arch2. In order to evaluate the performance of Two_Arch2 on ManyOPs, we have compared it with several MOEAs on a wide range of benchmark problems with different numbers of objectives. The experimental results show that Two_Arch2 can cope with ManyOPs (up to 20 objectives) with satisfactory convergence, diversity, and complexity.