具有重组学习和混合变异的动态多种群粒子群优化算法  

Dynamic multi-population particle swarm optimization algorithm with recombined learning and hybrid mutation

作　　者：唐可心 梁晓磊 周文峰 马千慧 张煜[2] TANG Ke-xin;LIANG Xiao-lei;ZHOU Wen-feng;MA Qian-hui;ZHANG Yu(School of Automobile and Traffic Engineering,Wuhan University of Science and Technology,Wuhan 430065,China;School of Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)

机构地区：[1]武汉科技大学汽车与交通工程学院,武汉430065 [2]武汉理工大学物流工程学院,武汉430063

出　　处：《控制与决策》2021年第12期2871-2880,共10页Control and Decision

基　　金：国家自然科学基金项目(61603280,71874132)。

摘　　要：为解决粒子群优化算法中种群多样性与收敛性间的矛盾,提出一种具有重组学习和混合变异的动态多种群粒子群优化算法.该算法动态划分多种群并融入重构粒子作为引导因子,在增加种群多样性的同时保留优秀粒子的空间信息;在算法执行阶段对最优个体施加混合变异,基于时变概率实施反向学习策略或者邻域扰动操作,帮助粒子快速跳出局部困境,加强对附近区域内的精细搜索.基于14个多类型标准测试函数,并与其他的改进粒子群算法进行对比,验证了几种改进措施的有效性和叠加影响.为进一步探究概率性混合变异策略的敏感性,对变异方式及参数设置进行仿真实验,结果表明,所采用的极值扰动策略具有显著的优势,合理地控制学习强度可以充分发挥反向学习的作用,并给出影响参数的建议取值范围.实验结果还表明,所提出的算法能够更好地平衡种群的开发与勘探能力,提高求解精度和收敛性能.To solve the contradiction between population diversity and convergence in particle swarm optimization,an improved particle swarm optimization algorithm,called dynamic multi-population particle swarm optimization with recombined learning and hybrid mutation,is proposed.In the proposed algorithm,a population is divided dynamically and a new particle is reconstructed as a guiding factor.It retains the spatial information of the excellent particles while increasing population diversity.During the execution of the algorithm,a hybrid mutation strategy is applied to adjust the optimal solution.The opposition-based learning and neighborhood-disturbance operations are implemented based on a time-varying probability,which help the particles jump out of the local dilemma quickly,and strengthen good searching in the nearby areas.The effectiveness and superposition effects of several proposed improvement operations compared with several improved particle swarm algorithms based on a set of 14 multi-type benchmark functions are verified.In order to further explore the sensitivity of probability-based hybrid mutation strategy,a large number of simulation experiments are carried out to analyze the mutation mode and parameter settings.The results show that the disturbed extreme strategy has significant advantages.Controlling the learning intensity reasonably can make the opposition-based learning show better performances,furthermore,a suggested value range is given.Finally,experimental results show that the proposed algorithm can get a better balance between the exploitation and exploration for the swarm searching and improve the solution accuracy and convergence performance.

关 键 词：粒子群算法 重构粒子 反向学习 极值扰动 概率选择 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]