摘要
针对粒子群优化算法易陷入局部极值和进化后期收敛速度缓慢的问题,提出基于Tent混沌序列的粒子群优化算法,应用Tent映射初始化均匀分布的粒群,提高初始解的质量,设定粒子群聚集程度的判定阈值,并引入局部变异机制和局部应用Tent映射重新初始化粒群的方法,增强算法跳出局部最优解的能力,有效避免计算的盲目性,从而加快算法的收敛速度。仿真实验结果表明,该算法是有效的。
Aiming at the problems of easily getting into the local optimum and slowly converging speed of the Particle Swarm Optimization（PSO） algorithm, a new PSO algorithm based on Tent chaotic sequence is proposed. The uniform particles are produced by Tent mapping so as to improve the quality of the initial solutions. The decision threshold of particles focusing degree is employed, and the local mutation mechanism and the local reinitializing particles are introduced in order to help the PSO algorithm to break away from the local optimum, whiek can avoid the redundant computation and accelerate the convergence speed of the evolutionary process. Simulation experimental results show this algorithm is effective.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第4期180-182,186,共4页
Computer Engineering
基金
陕西省教育厅科研计划基金资助项目(09JK335)
关键词
粒子群优化算法
TENT映射
变异机制
判定阈值
收敛速度
Particle Swarm Optimization（PSO） algorithm
Tent mapping
mutation mechanism
decision threshold
convergence speed
