计算机应用与软件2011，Vol.28Issue(6)：289-292,4.
一种自适应惯性权重的粒子群优化算法
A PARTICLE SWARM OPTIMISATION WITH ADAPTIVE INERTIA WEIGHT
郭长友1
作者信息
- 1. 德州学院计算机系,山东德州,253023
- 折叠
摘要
Abstract
In order to get a better balance between global search ability and local search capability of the particle swarm optimisation, the relationship between the inertia weight, the particle fitness and population size, as well as the dimensions of searching space is analysed, and the particle inertia weight is defined as a function of them three. By updating the inertia weight of every particle in each iteration, the selfadaptive adjustment between global search ability and local search ability is achieved. An improved particle swarm optimisation is brought up in combination with the population dynamic management strategy. The new algorithm is proved to have stronger global optimisation capability and higher search efficiency through the comparison of it with existing inertia weight adjustment algorithms using a couple of commonly used testing functions.关键词
粒子群算法/自适应惯性权重/种群规模/搜索空间维度/粒子适应度/动态管理种群Key words
Particle swarm optimization/ Adaptive inertia weight/ Population size/ Search space dimension/ Particle fitness / Dynamicmanagement of populations引用本文复制引用
郭长友..一种自适应惯性权重的粒子群优化算法[J].计算机应用与软件,2011,28(6):289-292,4.
