自适应分组和拥挤距离更新的多目标狼群算法  

Multi-objective wolf pack algorithm based on adaptive grouping strategy and crowding distance

作　　者：赵嘉[1,2] 吕丰 肖人彬[3] 樊棠怀[1,2] 董文飞 王晖[1,2] ZHAO Jia;LV Feng;XIAO Ren-bin;FAN Tang-huai;DONG Wen-fei;WANG Hui(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330000,China;Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City,Nanchang Institute of Technology,Nanchang 330099,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]南昌工程学院信息工程学院,南昌330000 [2]南昌工程学院南昌市智慧城市物联感知与协同计算重点实验室,南昌330099 [3]华中科技大学人工智能与自动化学院,武汉430074

出　　处：《控制与决策》2024年第11期3772-3780,共9页Control and Decision

基　　金：创新科技2030-“新一代人工智能”重大项目(2018AAA0101200);国家自然科学基金项目(52069014)。

摘　　要：鉴于狼群算法在单目标优化问题中具有良好的求解能力,借助狼群的生物习性并用于求解多目标优化问题,提出自适应分组和拥挤距离更新的多目标狼群算法(MOWPA-AG).首先,模拟狼群中的家族聚集性,提出兼顾种群多样性和分散搜索的自适应分组策略,对种群进行分层并帮助种群扩散检索Pareto最优解;然后,设计基于拥挤距离的群体更新机制,使种群保持快速进化的同时获得最优解集;为验证算法的性能,在9种不同的基准测试问题上进行测试,并与经典及新进多目标优化算法进行比较以验证MOWPA-AG的有效性;最后,将MOWPA-AG用于解决实际工程四杆桁架结构问题,以体现所提出算法的普适性.In view of the wolf pack algorithm has good solving ability in single objective optimization problems,a multi-objective wolf pack algorithm(MOWPA-AG)based on adaptive grouping and updating of crowded distance is proposed by taking the advantages of the wolf pack biological habit and being used to solve multi-objective optimization problems.Firstly,an adaptive grouping strategy considering population diversity and dispersed search is proposed to simulate family aggregation in wolf packs.The strategy stratifies populations,separates populations and helps population diffusion search Pareto optimal solutions.Then,a population renewal mechanism based on crowding distance is designed,which enables the population to maintain rapid evolution while obtaining the optimal solution set.In order to verify the performance of the proposed algorithm,nine different benchmark testing problems are tested,and the effectiveness of the proposed algorithm is verified by comparing with other classic and recent multi-objective optimization algorithms.Finally,the MOWPA-AG is applied to solve the problem of four-bar truss structure in practical engineering,which shows the universality of the proposed algorithm.

关 键 词：群智能算法 多目标优化 狼群算法 PARETO最优 自适应分组 工程优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]