摘要
为了克服标准粒子群优化算法(PSO)后期收敛速度慢、容易陷入局部最优等缺点,借鉴人工蜂群算法的思想,提出了一种提高收敛速度并且带有自适应逃逸功能的粒子群优化算法(FAPSO)。算法中每进化一次粒子搜索两次:一次全局搜索,一次局部搜索。当粒子陷入局部最优时,通过逃逸功能使粒子重新搜索。8个经典基准测试函数仿真结果表明,改进的粒子群优化算法在收敛速度和寻优精度上均有提高,相对于目前常用的改进粒子群优化算法如CLPSO等,t检验结果说明,新算法具有明显的优势。
In order to overcome the drawbacks of Particle Swarm Optimization （PSO） that converges slowly at the last stage and easily fails into local minima, this paper proposed a new PSO algorithm with convergence acceleration and adaptive escape （FAPSO） inspired by the Artificial Bee Colony （ABC） algorithm. For each particle, FAPSO conducted two search operations. One was global search and the other was local search. When a particle got stuck, the adaptive escape operator was used to search the particle again. Experiments were conducted on eight classical benchmark functions. The simulation results demonstrate that the proposed approach improves the convergence rate and solution accuracy, when compared with some recentlv nrot^osed PSO versions, such as CLPSO. Besides. the resuhs of t-test show clear superiority.
出处
《计算机应用》
CSCD
北大核心
2013年第5期1308-1312,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61261039)
江西省自然科学基金资助项目(20122BAB201043)
关键词
粒子群优化算法
全局搜索
局部搜索
快速收敛
自适应逃逸
Particle Swarm Optimization （PSO）
global search
local search
fast convergence
adaptive escape
