基于协同进化的多目标约束进化算法  

Multi-Objective Constrained Evolutionary Algorithm Based on Coevolution

作　　者：刘仁云 张旭 姚亦飞 于繁华 LIU Renyun;ZHANG Xu;YAO Yifei;YU Fanhua(College of Mathematics,Changchun Normal University,Changchun 130032,China;College of Computer Science and Technology,Changchun Normal University,Changchun 130032,China;College of Computer Science and Technology,Beihua University,Jilin 132013,China)

机构地区：[1]长春师范大学数学学院,长春130032 [2]长春师范大学计算机科学与技术学院,长春130032 [3]北华大学计算机科学与技术学院,吉林吉林132013

出　　处：《吉林大学学报（信息科学版）》2023年第2期321-328,共8页Journal of Jilin University（Information Science Edition）

基　　金：吉林省科技厅基金资助项目(20200201276JC);吉林省教育厅基金资助项目(20200822KJ)。

摘　　要：针对约束多目标优化算法(COA:Constrained Optimization Algorithms)中存在的难以有效兼顾收敛性和多样性的问题,提出了采用协同进化策略的多目标优化算法(CoMaC)。首先,将一个COA转化为一个带动态约束处理的多目标进化算法。然后采用差分进化(DE:Differential Evolution)生成第1种群,并将其中的已知可行解选入第2种群,并与第1种群协同进化。第1种群通过保持原约束条件的全局搜索加快收敛。第2种群通过局部搜索进化,保持并获得更多可行解。最后采用标准约束多目标测试函数进行实验,以测试所提出算法的性能。实验结果表明,与使用惩罚函数处理约束问题(PF:Penalty Function)和使用动态处理约束边界方法(DCMaOP:Dynamic Constrained Many Objective optimization Problem)相比,所提算法在反向世代距离(IGD:Inverted Generational Distance)和超体积(HV:Hypervolume)两个指标上均取得了良好的结果,说明所提算法可以有效地兼顾收敛性和多样性。A CoMaCOA(Co-evolution Multi-Objective Constrained) optimization algorithm is proposed to deal with the problem that it cannot be combined convergence and diversity effectively in multi-objective COA(Constrained Optimization Algorithms). First, a COA is transformed into the multi-objective evolutionary algorithm with dynamic constraint processing. Then, DE(Differential Evolution) is used to generate the first population. The second population is generated by the known feasible solution in the first population and coevolved with the first. The first population accelerates convergence by global search that does not deal with constraints. The second population evolves through local search to maintain and obtain more feasible solutions. Finally, the standard constrained multi-objective test function is used for experiments in order to test the performance of the proposed algorithm. The experiment result shows that the proposed algorithm achieves good results on both IGD(Inverted Generational Distance) and HV(Hypervolume), comparing with PF(Penalty Function) method and dynamic boundary processing to constrain problem DCMaOP(Dynamic Constrained Many Objective optimization Problem). It shows that the algorithm is both effective in convergence and diversity.

关 键 词：多目标进化算法 动态约束处理 协同进化 全局搜索 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]