计算机应用与软件2011，Vol.28Issue(10)：271-274,4.
基于改进惯性权重的粒子群优化算法
PARTICLE SWARM OPTIMISATION ALGORITHM BASED ON MODIFIED INERTIA WEIGHT
王洪涛 1任燕1
作者信息
- 1. 河南理工大学数学与信息科学学院 河南焦作454000
- 折叠
摘要
Abstract
As the inertia weight is an important parameter in particle swarm optimisation to balance global search and local search, a new PSO algorithm based on modified inertia weight is proposed. In initial stage of the evolution, the algorithm uses the strategy of dynamic and self-adaptive inertia weight based on different dimensions and different particles to accelerate the convergent speed. In later stage of the evolution, it uses the strategy of linear degressive inertia weight ( LDIW), at the same time introduces timely the chaotic mutation to increase the diversity of population for preventing the local optimum. Testing results on five typical test functions show that this algorithm improves the performances on speed of convergence, precision of convergence, stability and capacity of global search to a great extent than those of LDIW-PSO.关键词
粒子群优化/惯性权重/动态/混沌/维变异Key words
Particle swarm optimisation/Inertia weigh/Dynamic/Chaos/Dimension mutation分类
信息技术与安全科学引用本文复制引用
王洪涛,任燕..基于改进惯性权重的粒子群优化算法[J].计算机应用与软件,2011,28(10):271-274,4.
