基于聚类的昂贵多目标优化代理辅助进化算法

doi:10.15960/j.cnki.issn.1007-6093.2022.04.003

运筹学学报 ›› 2022, Vol. 26 ›› Issue (4): 31-42.doi: 10.15960/j.cnki.issn.1007-6093.2022.04.003

基于聚类的昂贵多目标优化代理辅助进化算法

白富生^1*, 陈姣伶²

1. 重庆师范大学重庆国家应用数学中心, 重庆 401331;
2. 重庆师范大学数学科学学院, 重庆 401331

收稿日期:2021-06-25 发布日期:2022-11-28
通讯作者: 白富生 E-mail:fsbai@cqnu.edu.cn
基金资助:
国家自然科学基金(Nos. 11991024, 11871128), 重庆自然科学基金(No. cstc2019jcyj-msxmX0368), 重庆市技术创新与应用发展专项重点项目 (No. cstc2021jscx-jbgsX0001), 重庆市教委科学技术研究计划重点项目 (No. KJZD-K202114801)

A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

BAI Fusheng^1*, CHEN Jiaoling²

1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China;
2. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China

Received:2021-06-25 Published:2022-11-28

摘要/Abstract

摘要： 针对目标函数估值昂贵的多目标优化问题, 提出了基于聚类的代理辅助进化算法。在MOEA/D算法的框架下, 对种群进行聚类, 并通过权重向量的邻域选出种群子集, 在子集上使用径向基插值函数辅助的差分进化算法得到新解, 对种群进行更新。在7个DTLZ标准测试问题上进行了数值实验, 计算结果表明本文提出的算法比新近提出的多目标邻域回归优化(MONRO)算法具有优势。

关键词: 多目标优化, 代理辅助进化算法, 径向基函数, 聚类

Abstract: A clustering-based surrogate-assisted evolutionary algorithm is proposed for computationally expensive multi-objective optimization problems. Under the framework of MOEA/D, the population is partitioned into several clusters, and the population subsets are formed via the neighbourhood of the weights. Then the radial basis function surrogate-assisted differential evolution algorithm is used to generate new solution points from the formed subsets, and the population is updated using the generated new solution. Numerical experiments have been undertaken on 7 DTLZ test problems, and the computational results indicate that the proposed evolutionary algorithm has advantages over the newly developed multi-objective neighborhood regression optimization (MONRO) algorithm.

Key words: multi-objective optimization, surrogate-assisted evolutionary algorithm, radial basis function, clustering

中图分类号:

白富生, 陈姣伶. 基于聚类的昂贵多目标优化代理辅助进化算法[J]. 运筹学学报, 2022, 26(4): 31-42.

BAI Fusheng, CHEN Jiaoling. A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization[J]. Operations Research Transactions, 2022, 26(4): 31-42.

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基于聚类的昂贵多目标优化代理辅助进化算法

A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

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