Multi-objective particle swarm optimization by fusing multiple strategies  

多策略融合改进的多目标粒子群算法

作　　者：XU Zhenxing ZHU Shuiran 许振兴;祝水然(安徽工业大学管理科学与工程学院,安徽马鞍山243032;天津大学电气自动化与信息工程学院,天津300072)

机构地区：[1]School of Management Science and Engineering,Anhui University of Technology,Ma'anshan 243032,China [2]School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China

出　　处：《Journal of Measurement Science and Instrumentation》2022年第3期284-299,共16页测试科学与仪器（英文版）

基　　金：National Natural Science Foundation of China(No.61702006);Open Fund of Key laboratory of Anhui Higher Education Institutes(No.CS2021-ZD01)。

摘　　要：To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.

关 键 词：multi-objective particle swarm optimization(MOPSO) spatially crowding congestion distance differential guidance weight hierarchical selection of global optimum 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]