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
约束多目标优化问题(CMOPs)除了需要解决多个相互冲突的目标之外,还需要满足一定的约束条件.针对约束造成CMOPs的Pareto前沿被分为多个部分,同时不可行区域的扩张进一步阻碍种群的探索,使种群陷入局部最优及其多样性急剧下降等问题,提出了一种基于动态ε约束处理机制的双种群约束多目标优化算法.该算法使用双种群协同进化策略,主种群考虑约束,通过改进的动态ε约束处理机制,充分利用不可行解提供的有效信息;而辅助种群不考虑约束,在平衡多样性的基础上向无约束Pareto前沿(UPF)快速收敛,并及时向主种群提供可行域外的有效信息,指导主种群的探索方向.实验结果表明所提出的算法在MW测试问题上相比其他算法更具竞争力.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据