基于共识粒子群的全局优化求解方法

doi:10.16182/j.issn1004731x.joss.20-FZ0371

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (10): 1936-1942.doi: 10.16182/j.issn1004731x.joss.20-FZ0371

基于共识粒子群的全局优化求解方法

陆湛文¹, 程新功^1,2, 张永峰^1,2,*

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

收稿日期:2020-04-30 修回日期:2020-06-17 出版日期:2020-10-18 发布日期:2020-10-14
作者简介:陆湛文(1996-),男,江苏宿迁,硕士生,研究方向全局优化理论与算法;程新功(1973-),男,山东济南,博士,教授,研究方向全局优化理论与算法及其在电力系统中的应用。
基金资助:
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)

Global Optimization Method Based on Consensus Particle Swarm Optimization

Lu Zhanwen¹, Cheng Xingong^1,2, Zhang Yongfeng^1,2,*

1. School of Electrical Engineering, University of Ji'nan, Ji'nan 250022, China;
2. Center of Intelligent System and Optimization Technology, University of Ji'nan-Global Optimal Big Data, Ji'nan 250022, China

Received:2020-04-30 Revised:2020-06-17 Online:2020-10-18 Published:2020-10-14

摘要/Abstract

摘要： 针对粒子群优化(PSO, particle swarm optimization)和高效全局优化(EGO, efficient global 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。

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

Abstract: 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.

Key words: particle swarm optimization, efficient global optimization algorithm, consensus-based PSO, surrogate model

中图分类号:

TP301.6

陆湛文, 程新功, 张永峰. 基于共识粒子群的全局优化求解方法[J]. 系统仿真学报, 2020, 32(10): 1936-1942.

Lu Zhanwen, Cheng Xingong, Zhang Yongfeng. Global Optimization Method Based on Consensus Particle Swarm Optimization[J]. Journal of System Simulation, 2020, 32(10): 1936-1942.

参考文献 11

[1]	夏学文, 王博建, 金畅, 等. 一种自适应多种群的PSO算法[J]. 系统仿真学报, 2016, 28(12): 2887-2895, 2902.Xia Xuewen, Wang Bojian, Jin Chang, et al.Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm[J]. Journal of System Simulation, 2016, 28(12): 2887-2895, 2902.
[2]	Jones D R, Schonlau M, Welch W J.Efficient global optimization of expensive Black-box function[J]. Journal of Global Optimization (S0925-5001), 1998, 13: 455-492.
[3]	Cressie N.The Origins of Kriging[J]. Mathematical Geology (S0882-8121), 1990: 239-252.
[4]	Kennedy J, Eberhart R C.Particle swarm optimization[C]// IEEE International Conference on Neural Networks. Piscataway USA: IEEE Press, 1995: 1942-1948.
[5]	Zhang Y F, Chiang H D.A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization[J]. IEEE Transactions on Cybernetics (S2168-2267), 2017, 47(9): 2717-2729.
[6]	Mckay M D, Beckman R J, Conover W J.A comparison of three methods for selecting values of input variables in the analysis of output from a computer code Technometrics[J]. Technometrics (S0040-1706), 1979, 21(2): 239-245.
[7]	Xie X F, Zhang W J, Yang Z L.A dissipative particle swarm optimization[C]// IEEE International Conference on Evolutionary Computation, Honolulu, USA. USA: IEEE, 2002: 1456-1461.
[8]	Zhang W J, Xie X F.DEPSO: Hybrid particle swarm with differential evolution operater[C]// IEEE International Conference on System. Man and Cybernetics, Washington D C, USA. USA: IEEE, 2003: 3816-3821.
[9]	Ratnaweera A, Halgamuge S K.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Transaction on Evolutionary Computation (S1089-778X), 2004, 8(3): 240-255.
[10]	黄松, 田娜, 纪志成. 基于自适应变异概率粒子群优化算法的研究[J]. 系统仿真学报, 2016, 28(4): 874-879.Huang Song, Tian Na, Ji Zhicheng.Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability[J]. Journal of System Simulation, 2016, 28(4): 874-879.
[11]	Vesterstrom J, Thomsen R.A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems[C]// 2004 Congress on Evolutionary Computation. USA: IEEE, 2004: 1980-1987.

[1]	马立新, 程颍. 计及可中断负荷的园区综合能源系统优化调度[J]. 系统仿真学报, 2022, 34(4): 817-825.
[2]	张凯庆, 嵇启春. 速度时变的多中心半开放式车辆路径问题研究[J]. 系统仿真学报, 2022, 34(4): 836-846.
[3]	陈魁, 毕利, 王文雅. 柔性作业车间AGV与机器双资源集成调度研究[J]. 系统仿真学报, 2022, 34(3): 461-469.
[4]	孙晓安, 栾小丽, 刘飞. 基于智能优化灰色模型的电子固废预测[J]. 系统仿真学报, 2022, 34(3): 536-542.
[5]	魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874.
[6]	牛侃, 李冰, 付强. 基于混沌扰动机制粒子群算法的战场频率分配方法[J]. 系统仿真学报, 2021, 33(8): 1905-1913.
[7]	孟建军, 王终军, 胥如迅, 李德仓. 基于模糊复合策略的高速列车主动悬挂控制研究[J]. 系统仿真学报, 2021, 33(7): 1554-1564.
[8]	魏瑞轩, 吴子沉. 无人机集群实时任务分配方法研究[J]. 系统仿真学报, 2021, 33(7): 1574-1581.
[9]	刘晓琳, 吴竟祎. 飞机舵机电液加载系统控制补偿器设计与仿真[J]. 系统仿真学报, 2021, 33(5): 1031-1038.
[10]	汪润鸿, 王红军, 邹湘军, 曾泽钦, 李慧, 黄钊丰, 刘伟良. 基于混合粒子群算法的组合式变速箱传动比优化研究[J]. 系统仿真学报, 2021, 33(4): 825-836.
[11]	陈魁, 毕利. 改进粒子群算法在考虑运输时间下的FJSP研究[J]. 系统仿真学报, 2021, 33(4): 845-853.
[12]	孙宇贞, 唐毅伟, 李帅. 基于改进粒子群算法的超超临界燃煤机组负荷系统建模[J]. 系统仿真学报, 2021, 33(4): 875-882.
[13]	胡小曼, 王艳, 纪志成. 模糊信息粒化与改进RVM的滚动轴承寿命预测[J]. 系统仿真学报, 2021, 33(11): 2561-2571.
[14]	蔡敏, 王艳, 纪志成. 基于多策略融合量子粒子群算法的MOFFJSP研究[J]. 系统仿真学报, 2021, 33(11): 2615-2626.
[15]	曹振乾, 印江, 张津华. 基于改进粒子群算法的主蒸汽温度系统辨识[J]. 系统仿真学报, 2021, 33(10): 2411-2419.

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 11

相关文章 15

编辑推荐

Metrics

本文评价