火力与指挥控制2017，Vol.42Issue(8)：120-122,3.DOI:10.3969/j.issn.1002-0640.2017.08.027
一种改进的粒子群优化算法
An Improved Particle Swarm Optimization Algorithm
摘要
Abstract
In view of the basic particle swarm optimization algorithm exits the slow speed convergence,low efficiency, and is easy to fall into the local optimum. In order to better balance the global and local search capability, the shrinkage factor is introduced into the particle swarm optimization algorithm. The particle of the population not only learn from the best particle, but also learn from all the particles in the algorithm,the diversity of particles is increased, The experimental results show that the improved particle swarm optimization algorithm can improve convergence speed and efficiency, and avoid the generation of local optimal solution comparing with the basic ant colony algorithm.关键词
粒子群/全局最优/局部最优/学习规则Key words
particle swarm/global optimum/local optimum/learning rule分类
信息技术与安全科学引用本文复制引用
任贺宇,郭磊,赵开新..一种改进的粒子群优化算法[J].火力与指挥控制,2017,42(8):120-122,3.基金项目
国家自然科学基金(61174085) （61174085）
河南省高等学校重点科研基金资助项目(16A520084) （16A520084）
