基于共识粒子群的全局优化求解方法  

Global Optimization Method Based on Consensus Particle Swarm Optimization

作　　者：陆湛文 程新功[1,2] 张永峰 Lu Zhanwen;Cheng Xingong;Zhang Yongfeng(School of Electrical Engineering,University of Ji'nan,Ji'nan 250022,China;Center of Intelligent System and Optimization Technology,University of Ji'nan-Global Optimal Big Data,Ji'nan 250022,China)

机构地区：[1]济南大学自动化与电气工程学院,山东济南250022 [2]济南大学-铨优智能系统与优化技术研究中心,山东济南250022

出　　处：《系统仿真学报》2020年第10期1936-1942,共7页Journal of System Simulation

基　　金：山东省自然科学基金(ZR2019QEE019);山东省高等学校青创科技支持计划(2019KJN029)。

摘　　要：针对粒子群优化(PSO,particleswarmoptimization)和高效全局优化(EGO,efficientglobal optimization)两种算法的特点,提出一种共识粒子群和局部代理模型协同的全局黑箱优化算法(CPSO-LSM,consensus particle swarm optimization and local surrogate model)。该算法固定PSO算法周期对粒子进行分群并在粒子达成共识后停止,将每群粒子周围的优质子区域输出作为代理模型的建模区域,通过比较各区域最优值获得高质量最优解甚至全局最优解。不仅避免了PSO冗长的计算过程、提高了建立代理模型的速度和精度还可以避免陷入局部最优。通过对比其他算法在标准测试函数的仿真结果,CPSO-LSM具有较好的收敛速度和求解精度。According to the characteristics of particle swarm optimization(PSO)and efficient global optimization algorithm(EGO),a global black box optimization algorithm based on consensus particle swarm optimization and local surrogate model(CPSO-LSM)is proposed.The algorithm fixes the period of the PSO algorithm to group the particles and stops after the particles reach a consensus.The high-quality sub-regions around each group of particles are used as the modeling area of the surrogate model,and the high-quality optimal solution or global optimal solution is obtained by comparing the optimal values of each region.It can not only avoid the complex calculation of PSO,improve the speed and precision of establishing agent model,but also avoid falling into local optimum.By comparing the simulation results of other algorithms in standard test functions,CPSO-LSM has better convergence speed and solution accuracy.

关 键 词：粒子群算法 高效全局优化算法 共识粒子群 代理模型 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]