摘要
针对现有面向多目标优化问题的约束处理方法存在求解效率不足,基于分解策略的多目标进化算法受到约束限制导致求解性能低的问题,提出一种基于记忆策略的动态分解约束多目标进化算法.本文首先引入具有记忆功能的归档集,改进基于短暂忽略非容许解的约束处理方法,提高算法的求解鲁棒性.然后结合基于分解的多目标进化算法,设计一种动态分配搜索资源的策略,提高算法的寻优能力.最后将设计的算法用于求解约束多目标基准测试集和1个工程问题,仿真结果表明,本文所提出算法的性能优于对比算法.算法具有有效性和可行性,求解约束多目标优化问题具有较好的性能.
To solve the issue that the existing constraint-handling techniques for multi-objective optimization problems are not efficient,and the multi-objective evolutionary algorithm based on decomposition strategy has low performance on constrained multi-objective optimization problems due to the constraints,a dynamic decomposition-based multi-objective evolutionary algorithm with a memory mechanism is proposed.In this paper,a memory mechanism based on an archive is introduced to remedy the shortcomings in the constraint-handling techniques based on temporarily disregarding constraints.Then,combined with decomposition-based multi-objective evolutionary algorithm,a dynamic allocation of search resource strategy is designed to improve the performance of the proposed algorithm when solving constrained multi-objective optimization problems.Finally,the proposed algorithm is applied to solve the constrained multi-objective benchmark problems and one engineering problem.The experimental results show that the performance of the proposed algorithm is superior over the compared algorithms.The proposed algorithm is effective and feasible,and has good performance in solving constrained multi-objective optimization problems.
作者
陈创明
温洁嫦
CHEN Chuangming;WEN Jiechang(School of Applied Mathematics,Guangdong University of Technology,Guangzhou 510520,Guangdong,China)
出处
《汕头大学学报（自然科学版）》
2022年第1期56-64,共9页
Journal of Shantou University：Natural Science Edition
关键词
多目标优化
约束处理方法
进化算法
动态分解
multi-objective optimization
constraint-handling technique
evolutionary algorithm
dynamic decomposition
