摘要
针对粒子群优化算法容易陷入局部极值点以及进化后期收敛慢和优化精度较差等缺点,提出一种改进的自适应进化算法.该算法引入信息扩散函数,根据不同粒子的位置及对应适应值与当前群体最佳位置和最佳适应值的关系,控制粒子变尺度向群体当前最佳位置移动;基于多样性反馈机制动态调节惯性权值和控制粒子群的微变异.通过复杂基准函数的仿真优化结果表明,改进算法具有抑制早熟、收敛速度快、求解精度高的特点.
In allusion to particle swarm optimization algorithm（PSO） many defects such as being easy to trap into local optima,slow convergence in the end of evolution stage and low computational precision,proposes an improved adaptive evolutionary PSO algorithm.A pheromone diffusion function,which can control the degree of convergence of particles move to the best position,is designed by both taking into account these particles distribution and their fitness value.Adjusting inertial weight adaptively with diversity feedback control is built into the improved PSO,and makes use of mutation to greatly contribute to breaking away from local optima.Experiments on optimization of high-dimension benchmark functions show that the improved algorithm can find better optima with converges faster,and prevent more effectively the premature convergence.
出处
《微电子学与计算机》
CSCD
北大核心
2011年第5期11-14,17,共5页
Microelectronics & Computer
基金
重庆市教委科学技术研究项目(KJ091309)
关键词
粒子群优化
信息扩散
多样性反馈
变异
早熟收敛
Particle Swarm Optimization
pheromone diffusion
diversity feedback
mutation
premature convergence
