混合策略改进的粒子群算法
Particle swarm algorithm improved by hybrid strategies
朱茂桃 1刘欢 1吴佘胤 1商高高1
作者信息
- 1. 江苏大学汽车与交通工程学院,江苏镇江 212013
- 折叠
摘要
针对粒子群算法易陷入局部最优、收敛精度低、收敛速度慢等缺陷,提出了基于混合策略的改进粒子群算法.使用融合Circle映射与精英反向学习的策略初始化种群,提升初始种群的质量,同时加快收敛速度;在粒子速度更新方式中引入蜘蛛移动策略平衡算法的全局搜索与局部搜索;提出了基于自适应t分布的变异策略,增强算法全局搜索和跳出局部最优能力;对15个单峰和多峰函数进行仿真实验,与其他3种算法进行了对比分析,结果表明:所提出的改进算法具有很强的寻优能力与稳定性.
Abstract
To remedy the defects of particle swarm algorithms,including the local optimum,low convergence accuracy,and slow convergence speed,this paper proposes an improved particle swarm algorithm based on hybrid strategies.First,the population is initialized by the fusion Circle mapping and the elite reverse learning to improve its quality and accelerate the convergence.Second,the spider mobile strategy is introduced in the particle speed update to balance the local and global search of the algorithm;then self-adaptive T distribution is proposed to enhance the algorithm's global search and its ability to jump out of local optimum.Finally,the 15 single-peak and multi-peak functions are simulated and analyzed with the other three algorithms.Our results show the improved algorithm possesses strong optimizing capacity and stability.
关键词
粒子群优化算法/蜘蛛优化/自适应t分布Key words
particle swarm optimization algorithm/spider optimization/adaptive t distribution引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据