动态环境下运用对称位移映射的PSO算法  

PSO Algorithm with Symmetric Displacement Mapping in Dynamic Environment

作　　者：刘子坤 李枚毅[1] 张晓[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机工程》2014年第11期200-204,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(61070232)

摘　　要：粒子群优化算法在求解动态优化问题时存在多样性缺失和寻优效率低的问题,为此,提出一种运用对称位移映射的双子群算法。该算法通过2组相互协同的主、辅子群并行地搜索变化的最优值。辅子群采取差异进化机制不断探索新环境,在感知环境变化时引入一种对称位移映射策略,使粒子对称分布在最优解的周围,以提高算法收敛到最优解的概率。使用MPB和DF1两种经典的Benchmark测试函数生成复杂的动态环境,对该算法进行实验仿真,结果表明,该算法能提高跟踪动态变化极值的准确性。Particle Swarm Optimization （ PSO ） algorithm is inclined to fall into diversity loss and low optimizing efficiency in dynamic environment. In this paper,a PSO algorithm with symmetric displacement mapping is proposed. The main subpopulation and assistant subpopulation particle swarm work with each other to search the changing global optimum by the parallel searching. The assistant subpopulation particle swarm uses differential evolutionary mechanism to constantly explore the new environment, when the environment is changed, and a symmetrical displacement mapping strategy is introduced to improve the convergence probability to the global optimum through symmetrical particle distribution surrounding the global optimum. The simulative environment in experiments is generated by MPB and DF1 two benchmark functions,the results demonstrate that the algorithm can improve the accuracy of tracking the changing global optimum.

关 键 词：粒子群优化 动态环境 优化问题 双子群协同 对称位移映射 差异进化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]