摘要
传统粒子群算法(PSO)容易早熟收敛,陷入局部最优,为此提出混沌动态多种群粒子群算法(CDMPSO),并将其应用在机器人三维路径规划中。通过引入混沌映射理论来提高粒子种群初始解的质量和分布均匀性,同时引入分组并行优化策略,依据适应度值采用中位数聚类的方法,将种群分为3个子种群并迭代进行实时动态调整,根据不同子种群的特点采用不同的方法来进行种群更新。在MATLAB软件中与传统PSO算法和自适应粒子群(APSO)算法进行对比实验,发现改进后的CDMPSO算法全局搜索范围更大,陷入局部最优次数更少,最终路径更短,从而验证了该改进算法是切实可行的。
Traditional particle swarm optimization(PSO)is easy to premature convergence and fall into local optimum.Therefore,chaotic dynamic multi swarm particle swarm optimization(CDMPSO)is proposed and applied to robot three-dimensional path planning.The chaotic mapping theory is introduced to improve the quality and distribution uniformity of the initial solution of the particle population.At the same time,the grouping parallel optimization strategy is introduced to divide the population into three sub populations by using the median clustering method according to the fitness value and iterate for real-time dynamic adjustment.Different methods are used to update the population according to the characteristics of different sub populations.Compared with traditional PSO algorithm and adaptive particle swarm optimization(APSO)algorithm in MATLAB software,the improved CDMPSO algorithm has larger global search range,fewer times of falling into local optimum and shorter final path,which verifies that the improved algorithm is feasible.
作者
王友运
徐坚磊
胡燕海
陈海辉
张行
Wang Youyun;Xu Jianlei;Hu Yanhai;Chen Haihui;Zhang Xing(School of Mechanical Engineering and Mechanics,Ningbo University,Ningbo 315211,China;Ningbo Hanggong Intelligent Equipment Co.,Ltd.,Ningbo 315311,China)
出处
《电子技术应用》
2024年第4期75-80,共6页
Application of Electronic Technique
基金
国家自然科学基金(51705263)。
关键词
路径规划
混沌映射
莱维飞行
高斯变异
动态多种群并行
path planning
chaotic mapping
Levy flight
Gaussian variation
dynamic multigroup parallelism
