| 基于信息扩散机制的双子群粒子群优化算法 |
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| 引用本文： | 范会联,仲元昌.基于信息扩散机制的双子群粒子群优化算法[J].系统仿真学报,2011,23(10):2125-2129. |
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| 作者姓名： | 范会联 仲元昌 |
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| 作者单位： | 1. 长江师范学院数学与计算机学院,涪陵,408100
2. 重庆大学通信工程学院,重庆,400030 |
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| 基金项目： | 科技部创新基金项目(10C26215115008); 重庆市教委科学技术项目(KJ111306) |
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| 摘 要： | 针对粒子群算法在多峰、高维函数的全局优化中易陷入局部极值的问题，在分析算法早熟收敛原因的基础上，提出一种基于信息扩散和多样性反馈机制的双子群粒子群优化算法。算法将粒子群划分力两纽搜紊方向相反的主、辅子群协同进化，通过引入信总扩散函数，根据不同粒子的位置及相应适应值与当前群体最佳位置和最佳适应值的关系，控制粒子变尺度向群体当万矿最佳位置移动，岁倦于多样性反馈机制动态调节惯性权重和分配主、辅子群的粒子数量。对基准函数的仿真优化结果表明，改进算法与其他PSO改进算法相比，具有抑制早熟、收敛速度快、求解精度高的特点。
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| 关 键 词： | 粒子群优化 信息扩散 启发式方法 多样性反馈 |
Two-subpopulation Particle Swarm Optimization Based on Pheromone Diffusion |
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| FAN Hui-lian,ZHONG Yuan-chang.Two-subpopulation Particle Swarm Optimization Based on Pheromone Diffusion[J].Journal of System Simulation,2011,23(10):2125-2129. |
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| Authors: | FAN Hui-lian ZHONG Yuan-chang |
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| Institution: | FAN Hui-lian1,ZHONG Yuan-chang2(1.School of Mathematics and Computer Science,Yangtze Normal University,Fuling 408100,China,2.College of Communication Engineering,Chongqing University,Chongqing 400030,China) |
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| Abstract: | Conventional algorithm of particle swarm optimization(PSO) is often trapped in local optima in global optimization of multimodal high-dimensional function.Analysis of the main causes of the premature convergence proposed an improved two-subpopulation PSO algorithm,based on the mechanism of pheromone diffusion and diversity feedback.The population was divided into main subpopulation particle swarm and assistant subpopulation particle swarm,whose search direction was inversed completely.A pheromone diffusion ... |
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| Keywords: | particle swarm optimization pheromone diffusion heuristic methods diversity feedback |
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