基于对称粒子群算法的动态环境问题求解  

Dynamic Environment Problem Solution Based on Symmetric Particles Algorithm

作　　者：成照乾[1] 王洪国[1] 邵增珍[1] 杨怡[2] 

机构地区：[1]山东师范大学信息科学与工程学院,济南250014 [2]山东师范大学管理与经济学院,济南250014

出　　处：《计算机工程》2010年第24期150-152,155,共4页Computer Engineering

基　　金：山东省科技攻关基金资助项目(2009GG10001008);山东省软科学研究计划基金项目(2009RKA285);济南市高校院所自主创新基金资助项目(200906001)

摘　　要：针对动态环境问题,提出一种具有自学习功能的对称粒子群算法(SymPSO)。该算法提出利用静态粒子群检测环境的变化,并基于对称粒子思想,在不增加运算量的前提下生成多个对称虚拟粒子群,扩大了种群搜索能力。为保证算法尽快逃离局部最优,给出广域学习策略,用以提高粒子的自学习能力。基于DF1环境下的仿真对比试验表明,SymPSO算法能快速跟踪最优值变化及迅速跳出局部最优,证实了其有效性。For the dynamic environment problem, this paper presents a self-learning function of the Symmetry Particle Swarm Optimization（SymPSO）. The algorithm proposes to detect changes of the environment by using a static virtual particle swarm, and based on the thought of symmetric particles, without increasing the computational complexity, generates multiple symmetric virtual population. It can significantly expand the ability of population. To ensure the algorithm to escape from local optimum as quickly as possible, this paper proposes wide-area learning strategies to enhance self-learning ability of particles. Simulation comparative tests based on DFI environment show that SymPSO algorithm can track the optimal value changes and escape from local optimum quickly, indicating the effectiveness of the algorithm.

关 键 词：粒子群优化 对称粒子群 静态粒子 广域学习 动态环境 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]