南昌工程学院学报2024，Vol.43Issue(1)：82-92,11.
基于动态ε约束处理机制的双种群约束多目标优化算法
A two-population constrained multi-objective optimization algorithm based on a dynamic ε constraint processing mechanism
涂继伟 1汪镭 1蔡振翔 1耿绍晋 1李东洋2
作者信息
- 1. 同济大学电子与信息工程学院,上海 201804
- 2. 同济大学中德工程学院,上海 201804
- 折叠
摘要
Abstract
Constrained multi-objective optimization problems(CMOPs)need to meet certain constraints in addition to solving multiple conflicting objectives.In view of the fact that the existence of constraints causes the Pareto front of CMOPs to be di-vided into multiple parts,while the expansion of infeasible regions further hinders the exploration of the population,causing the population to fall into a local optimum and its diversity to decrease dramatically,this paper proposes a dual-population-based optimization algorithm for constrained multi-objective optimization based on dynamic epsilon constraints processing mechanism.The algorithm uses a dual-population co-evolutionary strategy,in which the main population takes constraints in-to consideration and makes full use of the effective information provided by infeasible solutions through the improved dynam-ic epsilon constraint processing mechanism,while the auxiliary population does not consider constraints and converges rapid-ly to the Unconstrained Pareto Front(UPF)based on balancing the diversity,and provides effective information outside the feasible domain to the main population in a timely manner,and also provide the main population with effective information outside the feasible domain in time to guide the exploration direction of the main population.The experimental results show that the proposed algorithm is more competitive than other algorithms in the MW testing problems.关键词
约束处理机制/约束多目标优化/双种群/进化算法Key words
constraint processing mechanism/constrained multi-objective optimization/dual-population/optimization algo-rithm分类
信息技术与安全科学引用本文复制引用
涂继伟,汪镭,蔡振翔,耿绍晋,李东洋..基于动态ε约束处理机制的双种群约束多目标优化算法[J].南昌工程学院学报,2024,43(1):82-92,11.
