一种基于自适应搜索的多模态多目标优化算法  

A Multi-modal Multi-objective Optimization Algorithm Based on Adaptive Search

作　　者：李占山[1,2] 宋志扬 花昀峤 LI Zhan-shan;SONG Zhi-yang;HUA Yun-qiao(School of Software,Jilin University,Changchun 130012,China;School of Computer Science and Technology,Jilin University,Changchun 130012,China;Asset Management Division,Jilin University,Changchun 130012,China)

机构地区：[1]吉林大学软件学院,吉林长春130012 [2]吉林大学计算机科学与技术学院,吉林长春130012 [3]吉林大学资产管理处,吉林长春130012

出　　处：《东北大学学报（自然科学版）》2023年第10期1408-1415,共8页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61802056);吉林省自然科学基金资助项目(20180101043JC);吉林省发展和改革委员会产业技术研究与开发项目(2019C053-9).

摘　　要：为了解决目前基于分解的多模态多目标优化算法存在种群搜索能力不足,子种群中存在无用解和距离度量不具有普适性等问题,提出了一种基于自适应搜索的多模态多目标优化算法MOEA/D-AS.首先,该方法通过减少平均子种群的个体数量,进而增加参考向量的数量.其次,根据子种群当前状态自适应分配子种群的个体数量.最后,使用引入了局部种群信息的清除距离作为维护子种群的依据.将提出的算法与4种算法在2019年CEC多模态多目标测试问题和大规模多模态多目标测试问题上进行对比实验,实验结果表明,提出的算法可以有效解决多模态多目标优化问题.The current decomposition-based multi-modal multi-objective optimization algorithms have insufficient population search capability,useless solutions in sub-populations,and a non-universal distance metric.To address these issues,an adaptive search multi-modal multi-objective optimization algorithm MOEA/D-AS is proposed.Firstly,this method increases the number of reference vectors by reducing the size of the average sub-population.Secondly,the sub-populations are reallocated according to the current state of the sub-populations in the iteration.Finally,a clear distance based on local population information is introduced as the basis for modifying the sub-populations.The proposed algorithm is compared with four algorithms on the 2019 CEC multi-modal multi-objective test problems and the large-scale multi-modal multi-objective test problems for experiments.The experimental results show that the proposed algorithm can effectively solve the multi-modal multi-objective optimization problems.

关 键 词：多模态多目标优化算法 自适应搜索 子种群 局部信息 清除距离 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]