摘要
粒子群优化算法是一种基于仿生技术的启发式算法,针对粒子群优化算法存在易早熟现象,提出一种改进的粒子群优化算法.该算法给出了一种新的变异算子,该算子具有一定探索和开发能力,从而避免算法陷入局部最优.基于新变异算子给出一个新的粒子位置更新公式.根据系统稳定性理论,推出了算法的参数设置区域.最后,通过标准测试函数的性能测试,验证了改进粒子群优化算法收敛速度和求解精度.实验结果表明,该算法具有较好的收敛速度和求解精度.
Particle swarm optimization algorithm （PSO） is a heuristic algorithm based on bionic technology, To solve the premature convergence problem of the Particle Swarm Optimization, a modified PSO algorithm was proposed. In this algorithm, A new mutation operator with the exploration and exploitation ability is proposed to avoid falling into local optimal solutions. Based on the new mutation operator, a new updating formula of particle position is proposed. According to the system stability theory, the area of parameters of the modified algorithm is given. Finally, A performance test of benchmark functions is taken to confirm convergence speed and solution precision of the modified algorithm. The experimental results show that the modified algorithm has faster convergence speed and higher solution precision.
出处
《渤海大学学报（自然科学版）》
CAS
2017年第2期97-103,共7页
Journal of Bohai University:Natural Science Edition
基金
国家自然科学基金项目(No:11371071)
关键词
粒子群优化算法
变异算子
稳定性
函数优化
particle swarm optimization algorithm
mutation operator
stability
function optimization
