摘要
为了解决基本粒子群算法不易跳出局部最优的问题,提出了一种协同粒子群优化算法。在算法中通过加入权值递减的惯性因子和变异算子以克服基本PSO易早熟、不易收敛以及缺乏多样性的不足。将算法应用于极小极大选址问题的实验结果表明,算法能够有效地求解极小极大选址问题,具有较好的应用价值。
To solve the problem that particle swarm optimization algorithm is apt to trap in local optimum,a novel cooperative particle swarm optimization algorithm is proposed.In order to overcome the drawback of basic PSO,such as being subject to falling into local optimization,being poor in performance of precision and lack of diversity,an improved PSO,including the strategy for decreasing inertia weight and mutation operator,is proposed.The test experiments for solving min-max location problem show that the proposed solution can effectively reduce the cost of location problem,and has good application value
出处
《计算机工程与应用》
CSCD
北大核心
2011年第14期56-58,共3页
Computer Engineering and Applications
基金
河南省科技攻关项目(No.102102210483)
关键词
粒子群优化算法
协同粒子群
选址问题
particle swarm optimization algorithm
cooperative particle swarm
location problem
