具有博弈概率选择的多子群粒子群算法  

Multi-subgroup Particle Swarm Optimization Algorithm with Game Probability Selection

作　　者：田梦丹 梁晓磊 符修文 孙媛 李章洪 TIAN Meng-dan;LIANG Xiao-lei;FU Xiu-wen;SUN Yuan;LI Zhang-hong(College of Automobile and Traffic Engineering,Wuhan University of Science and Technology,Wuhan 430065,China;Institute of Logistics Science and Engineering,Shanghai Maritime University,Shanghai 201306,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]上海海事大学物流科学与工程研究院,上海201306

出　　处：《计算机科学》2021年第10期67-76,共10页Computer Science

基　　金：国家自然科学基金(61603280,61902238)。

摘　　要：针对粒子群算法在求解复杂多峰函数时存在早熟、易陷入局部最优、全局收敛性能差等缺陷,考虑种群结构、多模式学习和个体间博弈等因素,提出了具有博弈概率选择的多子群粒子群算法。该算法从改善群体多样性、提升个体搜索能力的角度出发,构建了动态多种群结构,并针对每个子群构建不同的学习策略(极端学习、复合学习、邻域学习和随机学习),子群间进行最优信息共享,形成异构多子群的多源学习方式;将进化博弈思想引入群体搜索过程中,个体通过收益矩阵和扎根概率进行策略概率选择,进入适合个体能力提升的子群进行学习。基于12个标准测试函数,针对算法中重要参数子群规模L的取值进行了组合实验,结果表明L取值N/2或N/3时,种群适应度分布及中位值具有明显优势;针对算法性能测试,利用不同维度下的标准测试函数与7种同类型算法进行对比实验,实验结果显示,改进算法在最优值、求解稳定性及收敛特征上整体优于对比算法,说明多源学习和博弈概率选择策略可以有效改善粒子群算法的性能。Aimed at solving the defects of premature and easy being trapped into the local optimum of particle swarm optimization(PSO),a new algorithm is proposed with considering the species group structure,multi-mode learning and individual game,which was named as multi-subgroup particle swarm optimization algorithm with game theory(MPSOGT).The proposed algorithm constructs a dynamic multi-subgroup structure and introduces different learning strategies to form a multi-source learning strategy with heterogeneous multiple subgroups.Then the evolutionary game theory is introduced into the process of population sear-ching.According to a dynamic payoff matrix and a dynamic rooted probability based on the game theory,each individual enters into a suitable subgroup randomly to enhance its searching ability.Based on twelve benchmark functions,combined experiments are carried out for subgroup size L.The results show that the population fitness and median have obvious advantages when the value of L is N/2 or N/3.Compared with seven similar algorithms under a set of twelve benchmark functions test with different scales,the results show that the performance of the improved algorithm is superior to the comparison algorithm in terms of optimal va-lue,solution stability and convergence characteristics.It is indicated that the proposed multi-source learning and game probability selecting strategies can effectively improve the performance of the PSO algorithm.

关 键 词：粒子群算法 动态异构多子群 博弈选择 收益矩阵 扎根概率 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]