基于PSO的队伍演化算法  

Team Evolutionary Algorithm Based on PSO

作　　者：陈伟[1] 项铁铭[1] 徐捷[1] 

机构地区：[1]杭州电子科技大学天线与微波技术研究所,杭州310018

出　　处：《模式识别与人工智能》2015年第6期521-527,共7页Pattern Recognition and Artificial Intelligence

摘　　要：粒子群优化算法(PSO)由于其原理简单、较易实现等特点,得到广泛研究和应用.为加快优化速度,提高收敛精度,文中提出基于PSO的队伍演化算法.该算法将优化过程分为两个阶段:第一阶段为保持多样性,把队员分成若干个初级队伍并行优化,形成高级队伍;后一阶段为提高收敛速度,仅优化高级队伍.在整个优化过程中,根据评估队员所取得的成绩,动态控制队员的调整步长和最大调整空间,同时产生教练组,为队员的进步方向提供指导.通过高维多峰测试函数进行测试对比,验证文中算法的优越性和有效性.Particle swarm optimization （ PSO） is widely studied and applied due to its simple principle and easy implementation. Aiming at improving the convergence speed and the search precision, an algorithm based on PSO, team evolutionary algorithm （ TeamEA） , is presented. The optimization process of this algorithm is divided into two stages. At the first stage, to keep the diversity the players are divided into junior teams to optimize and the senior team is formed. At the second stage, to improve the convergence speed, only the senior team is optimized. In the process of the whole optimization, by evaluating the achievements of the players, the adjustment of step-length and the maximum space are controlled, and the coaching staff is formed to guide the progress direction of the players. Results on high-dimensional multimodal test functions validate the superiority and effectiveness of the proposed algorithm.

关 键 词：粒子群优化算法( PSO) 队伍演化算法( TeamEA) 并行优化 动态控制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]