融合聚类和小生境搜索的多模态多目标优化算法  

A multimodal multi-objective optimization algorithm with clustering and niching searching

作　　者：顾清华[1,2,3] 唐慧 李学现 江松 GU Qinghua;TANG Hui;LI Xuexian;JIANG Song(School of Resources Engineering,Xi’an University of Architecture&Technology,Xi’an 710055,China;Xi’an Key Laboratory for Intelligent Industrial Perception,Calculation and Decision,Xi’an 710055,China;Xi’an Youmai Intelligent Mining Research Institute,Xi’an 710055,China;School of Management,Xi’an University of Architecture&Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055 [2]西安市智慧工业感知计算与决策重点实验室,陕西西安710055 [3]西安优迈智慧矿山研究院有限公司,陕西西安710055 [4]西安建筑科技大学管理学院,陕西西安710055

出　　处：《智能系统学报》2023年第5期1127-1141,共15页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金项目(52074205);陕西省自然科学基础研究计划(2020JC-44)。

摘　　要：针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。In order to address the problems associated with maintaining population diversity and the insufficient number of equivalent Pareto-optimal solutions for multimodal multi-objective optimization,this study proposes a multimodal multi-objective optimization algorithm combining clustering and niche search(CSSMPIO).In the proposed algorithm,a clustering-based special crowding distance method is designed to initialize the population.Additionally,self-organized speciation is introduced to form stable niches,facilitating parallel searching and maintaining equivalent Pareto-optimal solutions.Subsequently,a non-dominated special crowding distance is introduced to realize individual selection and an elite learning strategy,circumventing premature convergence.The algorithm has been simulated using seven other state-of-the-art algorithms on 14 multimodal multi-objective optimization problems and was tested and analyzed using the Wilcoxon rank sum test.The results reveal that the general performance of CSSMPIO is superior to that of the com-pared algorithms.Finally,the CSSMPIO algorithm is applied to the map-based test problem,which confirms the effect-iveness of the algorithm.

关 键 词：多模态多目标优化 鸽群优化算法 聚类策略 小生境搜索 非支配排序 精英学习策略 多样性 地图测试应用 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]