一种新的协同多目标粒子群算法  

A new kind of cooperative multi-objective particle swarm optimization algorithm

作　　者：周苗[1] 陈义保[1] 刘加光[1] 

机构地区：[1]烟台大学机电汽车工程学院,山东烟台264005

出　　处：《山东理工大学学报（自然科学版）》2008年第5期6-10,共5页Journal of Shandong University of Technology:Natural Science Edition

基　　金：国家自然科学基金资助项目(50465001)

摘　　要：提出了一种动态协同多目标粒子群算法,该算法采用一种新型群体停滞判别准则,自适应地决定子群体的新增和灭绝.用外部集合及精英保留策略保存Pareto有效解,用于指导整个粒子群的进化.通过子群体间的信息交换,使整个群体分布更均匀,并且避免了局部最优,保证了解的多样性.对弹簧的优化设计实例进行验证,与传统的多目标算法相比,该算法能够获得更优的结果.A new kind of dynamical cooperative multi-objective particle swarm optimization algorithm is proposed. Using a new kind of criterion for judging the stagnation of the population, the sub-population is adaptively added or deleted during the running of the algorithm, which makes the number of the sub-populations vary dynamically. Pareto efficient solutions are saved based on external sets and elites to keep the tactics. This is used to guide the evolution of the whole particle swarm. By the exchanges of information among the sub-populations, the whole particle swarm distributes uniformly and avoids local optimum, and the diversity of the solution is en- sured. With the example verification of spring optimization design, this new algorithm can obtain better results compared with the traditional multi-objective algorithm.

关 键 词：多目标优化 协同进化 粒子群算法 外部集合 自适应 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]