摘要
针对标准粒子群优化(PSO)算法易陷入局部最优、进化后期收敛速度慢和收敛精度低的缺点,提出一种基于高斯扰动的粒子群优化算法。该算法采用对粒子个体最优位置加入高斯扰动策略,有效地防止算法陷入局部最优,加快收敛并提高收敛精度。在固定评估次数的情况下,对8个常用的经典基准测试函数在30维上进行了仿真。实验结果表明,所提算法在收敛速度和寻优精度上优于一些知名的粒子群优化算法。
As standard Particle Swarm Optimization （PSO） algorithm has some shortcomings, such as getting trapped in the local minima, converging slowly and low precision in the late of evolution, a new improved PSO algorithm based on Gaussian disturbance （GDPSO） was proposed. Gaussian disturbance was put into in the personal best positions, which could prevent falling into local minima and improve the convergence speed and accuracy. While keeping the same number of function evaluations, the experiments were conducted on eight well-known benchmark functions with dimension of 30. The experimental results show that the GDPSO algorithm outperforms some recently proposed PSO algorithms in terms of convergence speed and solution accuracy.
出处
《计算机应用》
CSCD
北大核心
2014年第3期754-759,共6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
20132BAB211031)
江西省教育厅落地计划项目(KJLD13096)
江西教育厅科技项目(GJJ13761
GJJ13745)
关键词
粒子群优化算法
高斯扰动
快速收敛
全局搜索
Particle Swarm Optimization （PSO） algorithm
Gaussian disturbance
fast convergence
global search
