基于自适应Tent混沌搜索的粒子群优化算法

摘要

To solve the premature convergence problem of Particle Swarm Optimization (PSO), a new PSO algorithm based on adaptive chaos search was proposed.The uniform particles were produced by Tent mapping so as to improve the quality of the initial solutions.Tent chaotic sequence based an optimal location was produced, and the adaptive adjustment of search scopes can avoid the redundant computation and accelerate the convergence speed of the evolutionary process.The experimental results show that the new introduced algorithm outperforms several other famous improved PSO algorithms on many well-known benchmark problems.%为解决粒子群优化算法易于陷入局部最优问题,提出基于自适应Tent混沌搜索的粒子群优化算法.应用Tent映射初始化均匀分布的粒群,并以当前整个粒子群迄今为止搜索到的最优位置为基础产生Tent混沌序列,混沌序列的搜索范围采用自适应调整方法.该方法可以有效避免计算的盲目性,还能够快速搜寻到最优解.实验表明该算法在多个标准测试函数下都超越了同类改进算法.