分递进层法的粒子群优化算法  

Particle Swarm Optimization Algorithm Based on Sub-inclusion Layer Method

作　　者：李茂林 吕柏权[1] 陆凯峰 

机构地区：[1]上海大学机电工程与自动化学院,上海200072

出　　处：《工业控制计算机》2018年第1期119-121,共3页Industrial Control Computer

摘　　要：基于人类社会发展改变的特点,提出了一种基于人类社会发展的粒子群算法:分递进层的粒子群算法,并将该改进算法应用于11个基准测试函数例子,测试其搜索精度和鲁棒性。仿真的结果表明了改进方法提高了算法的精度。In this paper,an improved particle swarm optimization algorithm is proposed based on the characteristics of human social development:the particle swarm optimization algorithm is divided into the progressive layers,and the improved algorithm is used to test and simulate the 11 benchmark functions respectively in terms of convergence speed and search precision.From the results of the simulations,it is proved that the proposed method can improve precision of the algorithm.

关 键 词：早熟收敛 收敛速度 搜索精度 搜索能力 粒子群优化算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]