内嵌区域震荡搜索的粒子群优化算法  

Regional shock search embedded Particle Swarm Optimization algorithm

作　　者：汤继涛[1] 戴月明[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机工程与应用》2013年第21期33-36,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61170120)

摘　　要：针对粒子群优化算法早熟收敛现象,提出了一种改进的粒子群优化算法。新算法在粒子群中的每个粒子吸引子的基础上引入了区域震荡搜索因子。每个粒子在协同收敛的同时,震荡搜索粒子极值位置周围区域,增加种群的多样性,提升算法的全局寻优能力,有效避免算法陷入局部收敛。仿真结果表明,改进后的算法在收敛精度上得到显著的改善。To solve the premature convergence problem of the Particle Swarm Optimization （PSO）, an improved PSO algorithm is proposed. On the basis of attractor of each particle in the particle swarm, region shock search factor is embedded in the new algorithm. During converging cooperatively, each particle searches the surrounding area of particle extremum position, increasing the diversity of the population, enhancing the ability of global optimization of the algorithm and avoiding algorithm to fall into local convergence effectively. The simulation results show that the improved algorithm gets a significant improvement in convergence accuracy.

关 键 词：粒子群优化 早熟收敛 区域震荡搜索 全局优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]