摘要
鉴于狼群算法在单目标优化问题上的优越表现,结合狼群的生物习性将其运用到多目标优化问题上,提出一种精英引导和信息交互的多目标狼群算法(MOWPA-EGII)。首先,提出精英引导策略,利用外部档案中的精英狼和当前子种群的头狼共同引导种群移动,让人工狼均匀地分布在整个搜索空间,增强算法的全局搜索能力;其次,设计信息交互机制,模拟狼群捕猎过程中的信息传递,具有不同优势的个体可以相互传递信息,保证狼群的捕猎效率,提高算法勘探Pareto最优解的能力;最后,加入变异算子,扰动人工狼的移动方向,让算法跳出局部最优,增强算法的局部搜索能力。为了验证MOWPA-EGII的有效性,将其与5种经典算法和10种新近算法进行比较,结果表明MOWPA-EGII拥有良好的收敛性和多样性,证明了所提算法具有较好的优化性能。
In consideration of the superior performance of the wolf pack algorithm on single-objective optimization problems,combining with the biological habits of wolves to apply it to multi-objective optimization problems,this paper proposed a multi-objective wolf pack algorithm with elite guidance and information interaction(MOWPA-EGII).Firstly,MOWPA-EGII proposed an elite guidance strategy,using the elite wolf in the external file and the head wolf of the current sub-population to jointly guide the population movement,so that the artificial wolves uniformly distributed in the whole search space,and the strategy enhanced the global search ability of the algorithm;Secondly,it designed the information interaction mechanism to simulate the information transfer in the wolf hunting process,so that individuals with different advantages could transfer information to each other to ensure the hunting efficiency of the wolves and improved the ability of the algorithm to explore the optimal solution of the Pareto;Finally,it added the mutation operator to perturb the moving direction of the artificial wolves,so as to let the algorithm jump out of the local optimum and enhance the local search ability of the algorithm.In order to verify the effectiveness of MOWPA-EGII,comparing it with 5 classical algorithms and 10 recent algorithms,and the results show that MOWPA-EGII possesses good convergence and diversity,which proves that the present algorithm has a better optimization performance.
作者
陈福军
吴润秀
肖人彬
王晖
赵嘉
Chen Fujun;Wu Runxiu;Xiao Renbin;Wang Hui;Zhao Jia(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Nanchang Key Laboratory of IoT Perception&Collaborative Computing for Smart City,Nanchang Institute of Technology,Nanchang 330099,China;School of Artificial Intelligence&Automation,Huazhong University of Science&Techno-logy,Wuhan 430074,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第8期2404-2411,共8页
Application Research of Computers
基金
国家自然科学基金资助项目(52069014)。
关键词
狼群算法
多目标优化
精英引导
信息交互
变异算子
wolf pack algorithm
multi-objective optimization
elite guidance
information interaction
variational operator
作者简介
陈福军(1997-),男,江苏连云港人,硕士研究生,主要研究方向为智能计算;通信作者:吴润秀(1971-),女,江西抚州人,教授,硕导,硕士,主要研究方向为群智能算法及应用(wrx@nit.edu.cn);肖人彬(1965-),男,湖北武汉人,教授,博导,博士,主要研究方向为群智能、涌现计算和复杂系统建模与仿真;王晖(1982-),男,湖北黄冈人,教授,硕导,博士,主要研究方向为群智能算法和进化计算;赵嘉(1981-),男,安徽安庆人,教授,硕导,博士,主要研究方向为智能计算、模式识别和大数据分析.
