基于PRS元模型的粒子群优化算法及其应用  

Application of PRS metamodel assisted PSO optimization

作　　者：梁靓[1] 熊会元[1,2] 揭皓翔 吴义忠[3] 

机构地区：[1]中山大学工学院,广州510006 [2]东莞中山大学研究院,广东东莞523808 [3]华中科技大学机械科学与工程学院,武汉430074

出　　处：《计算机应用研究》2016年第6期1653-1655,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(51175198);东莞市重大科技专项基金资助项目(2011215155);国家科技支撑资助项目(2012BAF16G02)

摘　　要：针对传统粒子群算法优化黑箱模型过程中存在巨大计算开销的问题,提出一种基于PRS元模型的改进粒子群优化算法——PPSO算法。在该算法迭代过程中,构建PRS元模型,利用其最优值点辅助粒子种群的更新,此外仅选择元模型预估集中优值集的粒子进行目标函数的计算仿真。将PPSO算法与基本粒子群算法、混沌粒子群算法进行数值测试对比,并应用于模糊控制器的优化设计,仿真结果表明该算法可减少真实估值次数,提高优化搜索能力。To reduce huge computational overheads when solving computationally expensive black-box optimization problems by conventional particle swarm optimization algorithm,this paper proposed an improved particle swarm optimization algorithm,called PPSO algorithm based on the PRS metamodel. In the iterative process of PPSO,it constructed the PRS metamodel based on sampling data,and used the optimal point of metamodel to assist the update of particle population. In addition,only selected the promising particles to perform actual function evaluations in order to reduce the computational cost. This paper it tested the new proposed method by several benchmark functions,compared it with basis PSO and CPSO,and then applied it into the design of fuzzy controller. Numerical tests show that the proposed algorithm can reduce the number of expensive simulations and improve the search ability.

关 键 词：粒子群优化 元模型 模糊控制器 全局优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]