摘要
针对微粒群算法(particle swarm optimization)收敛速度慢和早熟收敛的问题,提出一种基于二级搜索(Two steps search)和高斯学习(Gauss learning)相结合的粒子群优化算法(TGPSO).该算法借鉴人工蜂群算法能有效地进行局部搜索和全局搜索,并能在陷入局部极值时跳出局部极值的特点,从两方面对微粒群算法进行改进:通过二级搜索,强化较优粒子的局部搜索能力,可加快收敛速度;应用高斯学习的自适应逃逸能力,可有效地逃离局部最优点.在典型测试函数集上的仿真实验结果表明本文算法有较好的寻优性能并能快速地找到最优解.
In order to solve the problem of low convergence rate and Premature Convergence of particle swarm optimization,an improved particle swarm optimization algorithm is proposed which based on two steps search and Gauss learning. In combination with the strength of artificial bee colony（ABC） which can effectively carry out local and global search and jump out local extreme points when it gets into the local extreme,the particle swarm algorithm can be improved in two aspects. Firstly,two steps search can enhance the ability of local search of the optimal particle and consequently increase the convergence speed. Secondly,applying the adaptive escape ability of Gauss learning function can effectively escape from local optima. Simulation experimental results on benchmark functions show that the improved particle swarm optimization algorithm achieves better performance and can rapidly find the optimal solution.
出处
《小型微型计算机系统》
CSCD
北大核心
2014年第7期1636-1641,共6页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61261039)资助
江西省自然科学基金项目(20122BAB201043
20132BAB211031)资助
江西省教育厅科技项目(GJJ13763
GJJ13761)资助
关键词
粒子群优化算法
人工蜂群算法
二级搜索
高斯学习
particle swarm optimization（PSO）
artificial bee colony algorithm
two steps search
Gauss learning
