基于分区搜索和强化学习的多模态多目标头脑风暴优化算法  

Multimodal multi-objective brain storm optimization algorithm based onzoning search and reinforcement learning

作　　者：李鑫 余墨多 姜庆超[3] 范勤勤 Li Xin;Yu Moduo;Jiang Qingchao;Fan Qinqin(Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China;Key Laboratory of Control of Power Transmission&Conversion(Ministry of Education),Shanghai Jiao Tong University,Shanghai 200240,China;Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education,East China University of Science&Technology,Shanghai 200237,China)

机构地区：[1]上海海事大学物流研究中心,上海201306 [2]上海交通大学电力传输与功率变换控制教育部重点实验室,上海200240 [3]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

出　　处：《计算机应用研究》2024年第8期2374-2383,共10页Application Research of Computers

基　　金：教育部人文社科基金规划基金资助项目(23YJAZH029);上海市浦江人才计划资助项目(22PJD030);国家自然科学基金资助项目(61603244)。

摘　　要：维持种群多样性和提高算法搜索效率是多模态多目标优化亟需解决的两大问题。为解决以上问题,提出了一种基于分区搜索和强化学习的多模态多目标头脑风暴优化算法(MMBSO-ZSRL)。在MMBSO-ZSRL中,首先将决策空间分解为多个子空间以降低搜索难度和维持种群多样性;然后,使用SARSA(state-action-reward-state-action)算法来平衡头脑风暴算法的全局探索和局部开发能力;并使用特殊拥挤距离来挑选个体来指导种群进化。为了验证所提算法的性能,选取六种先进的多模态多目标优化算法来进行比较,并选取IEEE CEC2019多模态多目标问题基准测试集来对所有比较算法的性能进行测试。实验结果表明,MMBSO-ZSRL的整体性能要显著优于其他六种比较算法。MMBSO-ZSRL不仅可以找到多样性和逼近性更好的帕累托前沿,而且可以在决策空间找到更多的帕累托最优解。Maintaining population diversity and improving algorithm search efficiency are two major problems that need to be solved urgently in the multimodal multi-objective optimization.To address the above problems,this paper proposed a multimodal multi-objective brain storm optimization algorithm based on zoning search and reinforcement learning(MMBSO-ZSRL).In the MMBSO-ZSRL,the decision space was first decomposed into multiple subspaces to reduce the search difficulty and maintain the population diversity.Subsequently,the proposed algorithm used SARSA algorithm to balance the global exploration and local exploitation capabilities of the brain storm optimization algorithm.Additionally,the MMBSO-ZSRL utilized the special crowding distance to select individuals for guiding the population evolution.To verify the performance of the proposed algorithm,this paper selected six advanced multimodal multi-objective optimization algorithms and the IEEE CEC2019 multimodal multi-objective problem benchmark test suite for experiments.Experimental results demonstrate that the overall perfor-mance of the MMBSO-ZSRL is significantly better than that of compared algorithms.The proposed MMBSO-ZSRL can not only find the Pareto front with better diversity and approximation,but also find more Pareto optimal solutions in the decision space.

关 键 词：多模态多目标优化 头脑风暴优化算法 强化学习 SARSA算法 分区搜索 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]