文献类型：期刊文献

中文题名：基于密度聚类的多目标粒子群优化算法

英文题名：Multi-objective Particle Swarm Optimization Algorithm Based on Density Clustering

作者：王学武[1];闵永[1];顾幸生[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室

年份：2019

卷号：45

期号：3

起止页码：449

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金(61773165,61573144)

语种：中文

中文关键词：多目标优化;粒子群算法;密度聚类

外文关键词：multi-objective optimization;PSO;density clustering

摘要：提出了一种基于密度聚类的领导粒子选择策略的多目标粒子群优化算法。首先,将粒子进行分类;然后,对外部档案采用改进的循环拥挤距离排序,并将高斯变异引入到进化种群,在保持具有全局搜索能力的同时,也避免了陷入局部最优。对 WFG系列测试函数的仿真结果表明,与经典多目标优化算法相比,本文算法在解的收敛性和多样性等方面有显著的提升。
Aiming at the leader particle selection problem, we present a multi-objective particle swarm optimization algorithm based on density clustering, termed as DN-MOPSO. Firstly, the particles composed of evolutionary population and external archive are divided into several classes by using density clustering technique in decision variable space. And then, the particles in the evolutionary population select their respective global leader particles according to the classification results so as to achieve the balance between global search capabilities and local search capabilities. Moreover, when the non-dominated solutions exceed the maximum limit, the improved circular crowed distance will be utilized to sort the external archive so that the obtained solution set can be well the distributed. In addition, the Gauss mutation operator mechanism is introduced into the iterative process to ensure the proposed algorithm jumping out of premature and promote the search capacities. Finally, the proposed DN-MOPSO algorithm is tested on several WFG benchmarks functions and compared with some classic multi-objective optimization algorithms. Simulation results show that DN-MOPSO algorithm can achieve better approximation to the optimal Pareto front and get well-spread Pareto solutions. Moreover, this strategy in this paper can not only enlarge the search scope of the DNMOPSO algorithm, but also improve the search accuracy, meanwhile, maintain the diversity of non-dominated solution. Therefore, the convergence and diversity of the solutions obtained by DN-MOPSO algorithm are significantly improved. These simulation results also indicate that DN-MOPSO algorithm is highly feasible and competitive for solving multi-objective optimization problems.