摘要
针对传统的单种群粒子群优化算法易陷入局部最优、搜索精度低的问题,提出一种异构多子群粒子群算法。算法由自适应子群、精英子群和若干普通子群构成,精英子群由普通子群和自适应子群中的优秀个体组成,每个子群采用不同策略进行进化,根据种群的早熟收敛程度和粒子的适应度值自适应地调整惯性权重;自适应子群根据普通子群的适应度值和速度自适应调整飞行方向,采用免疫克隆选择算子对精英子群进行精细搜索,普通子群、自适应子群与精英子群之间通过迁移操作实现信息的充分交流。针对典型的Benchmark函数优化问题测试,仿真结果表明所提算法能较好地保持粒子多样性,收敛精度高且全局搜索能力强,具有良好的优化性能。
Conventional particle swarm optimization is easily trapped in local optima and has the problem of low search accuracy. This paper proposed a multi-swarm particle swarm optimization with heterogeneous search. The proposed algorithm consisted of one adaptive sub-swarm,one elite sub-swarm and several ordinary sub-swarm,particles in elite sub-swarm were outstanding individuals migrated from adaptive sub-swarm and ordinary sub-swarm. Each sub-swarm evolved with heterogeneous strategies. It changed the inertia weight adaptively according to the degree of population premature convergence. It adjusted the flight direction of the particles in adaptive sub-swarm according to fitness value and speed of ordinary sub-swarm. It employed the immune clonal selection operator for optimizing the elite sub-swarm while employed the migration scheme for the information exchange between elite sub-swarm and others sub-swarm. Experiments on four benchmark function show that the proposed method can maintain the diversity of particles with strong global search capability,and converge with high precision and with better optimization performance.
出处
《计算机应用研究》
CSCD
北大核心
2016年第3期677-681,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61174140)
中国博士后基金资助项目(2013M540628)
湖南省自然科学基金资助项目(14JJ3107)
关键词
粒子群优化
异构搜索
多子群
协同进化
多样性
克隆选择
particle swarm optimization（PSO）
heterogeneous search
multi-swarm
cooperative evolution
diversity
clonal selection
