计算机工程与应用2024，Vol.60Issue(9)：159-171,13.DOI:10.3778/j.issn.1002-8331.2307-0381
面向高维多目标优化的双阶段双种群进化算法
Dual-Stage Dual-Population Evolutionary Algorithm for Many-Objective Optimization
摘要
Abstract
As the number of objectives increases,the Pareto front of many-objective optimization problem becomes increas-ingly complex.Traditional decomposition-based many-objective evolutionary algorithms struggle to select populations with both good diversity and convergence characteristics.To address this issue,a novel dual-stage dual-population evolu-tionary algorithm for many-objective optimization is proposed.In this algorithm,the evolutionary process is divided into two stages.In the first stage,it determines whether the shape of the Pareto front is regular.In the second stage,it adjusts the weight vectors based on the shape of the Pareto front,ensuring that the population can achieve good diversity on both regular and irregular Pareto fronts.To perform weight vector adjustments without affecting the convergence of the algo-rithm,two populations are used for evolution:one main population evolves normally,and the other auxiliary population serves as the weight vectors.Finally,to obtain a set of weight vectors that adapt well to populations distributed on irregular Pareto fronts,the concept of energy balance in nature is introduced to collect a well-diverse auxiliary population as weight vectors.The proposed algorithm is compared with other algorithms on test problems with 3-10 objectives.Experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on the majority of the test problems.关键词
高维多目标优化/进化算法/双阶段/双种群/权重向量/能量平衡Key words
many-objective optimization/evolutionary algorithm/dual-stage/dual-population/weight vector/energy balance分类
信息技术与安全科学引用本文复制引用
曹嘉乐,杨磊,田井林,李华德,李康顺..面向高维多目标优化的双阶段双种群进化算法[J].计算机工程与应用,2024,60(9):159-171,13.基金项目
国家自然科学基金(61573157) （61573157）
广东省自然科学基金(2020A1515010691) （2020A1515010691）
广州市农业科技特派员项目(20212100036). （20212100036）
