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An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization
Complexity ( IF 2.462 ) Pub Date : 2021-01-13 , DOI: 10.1155/2021/4393818
Naili Luo; Wu Lin; Peizhi Huang; Jianyong Chen

In multimodal multiobjective optimization problems (MMOPs), multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space (N is population size). Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm.

中文翻译:

基于聚类辅助选择策略的多模态多目标优化进化算法

在多模式多目标优化问题(MMOP)中,应保留多个Pareto最优集,甚至一些良好的局部Pareto最优集,这可以为决策者提供更多选择。为了解决MMOP问题,提出了一种基于聚类的辅助选择策略的多模态多目标优化进化算法,提出了加法运算符和删除运算符综合考虑决策空间和目标空间的多样性。具体而言,在决策空间中,联合人口使用基于密度的聚类方法划分为多个聚类,旨在帮助加法算子增强人口多样性。然后,采用多个权向量将人口分为N目标空间中的次区域(N是人口规模)。此外,在删除运算符中,首先将最拥挤的子区域中的解决方案收集到先前的聚类中,然后删除最拥挤的聚类中最差的解决方案,直到剩下N个解。我们的算法与著名基准MMOP上的其他多峰多目标进化算法进行了比较。数值实验报告了我们提出的算法的有效性和优势。
更新日期:2021-01-13
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