基于自适应动态邻居和广义学习的改进粒子群算法  

Improved particle swarm optimization based on adaptive dynamic neighborhood and generalized learning

作　　者：刘衍民[1,2] 赵庆祯[2] 牛奔[3] 

机构地区：[1]遵义师范学院数学系,贵州遵义56300 [2]山东师范大学管理与经济学院,济南250014 [3]深圳大学管理学院,广东深圳518060

出　　处：《计算机应用》2010年第10期2578-2581,共4页journal of Computer Applications

基　　金：广东自然科学基金资助项目(9451806001002294);深港创新圈项目(200810220137A);贵州教育厅社科项目(0705204)

摘　　要：为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷,提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。在ADPSO算法中,根据每个粒子邻居中最好运行粒子的状态动态地调整邻居拓扑结构;每个粒子的学习样本包括全局最优粒子、自身最优粒子和粒子邻居中最优运行粒子;并且在新产生的粒子位置上,加上一个随机位置以增加粒子向全局最优解移动的概率。在基准函数的测试中,结果显示ADPSO算法比其他PSO算法有更好的运行效果,是求解多峰问题的一种有效算法。As Particle Swarm Optimization （PSO） may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed. In ADPSO, the neighbors of each particle were dynamically constructed in terms of the best performing particle among the current particle neighborhood. Then the learning mechanism of each particle was separated into three parts： its own historical best position, the best neighbor and the global best one. At the position of the new particle, a random position around itself was added to increase the probability for the particle to move to that promising region. The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.

关 键 词：自适应 粒子群算法 动态邻居 广义学习 多峰函数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]