摘要
针对粒子群算法由于多样性差而导致的算法搜索效率低、稳定性差的缺点,提出了一种拥有双层多群间协调机制的粒子群优化算法(DC-PSO).该算法包含多个下层工作粒子群及上层决策粒子群,下层粒子群进行最优粒子信息采集和迭代计算,上层粒子群处理信息和反馈决策信息,双层粒子群联合工作.同时采用指数函数递增分布的加速度因子控制各粒子群间的耦合性,从而改善了粒子群后期搜索效率低、稳定性差的状况.仿真实验结果表明DC-PSO算法在求解函数优化问题的有效性和优越性.
This paper proposes a particle swarm optimization algorithm(DC-PSO)which has double group of coordination mechanism that focus on remedy the limitation of particle swarm algorithm(PSO)with low efficiency and poor stability because of the lack of diversity.This particle swarm optimization algorithm includes multiple lower work and upper decision particle swarm.The lower work particle swarm is to do the information acquisition and iteration calculation,while the top particle swarm processes information and decision-making information feedback,and double particle swarm do the teamwork.At the same time,acceleration factor with increasing distribution exponential function controls the coupling among particle swarm,so as to improve the efficiency and stability of the particle swarm when they doing the search job.The result of simulation experiment shows the effectiveness and superiority of particle swarm optimization algorithm in solving the function optimization problem.
作者
孙文
卫文学
李国杰
SUN Wen;WEI Wen-xue;LI Guo-jie(College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266510,China)
出处
《微电子学与计算机》
CSCD
北大核心
2018年第11期24-27,共4页
Microelectronics & Computer
基金
国家重点研发计划项目(2016YFC0801400)
关键词
粒子群算法
群间协调
自适应搜索
双层粒子群
particle swarm optimization algorithm
group of coordination between
adaptive adjustment searching
double layers