动态拓扑结构混合粒子群算法及其应用

doi:10.3778/j.issn.1673-9418.2305101

摘要/Abstract

摘要： 针对传统粒子群算法面对较高维度参数整定问题时所表现出的寻优速度慢、易陷入局部最优解的问题，提出一种动态环形拓扑结构混合粒子群算法（DynRing-hfpso）。该算法以粒子群算法为基底，融合萤火虫算法的优点，通过定义选择逻辑使粒子在迭代过程中各自独立地交替进行全局搜索与局部探索，并以自适应的粒子速度与位置约束方法提高算法在迭代过程中信息的利用率。对粒子群拓扑结构进行改进，以动态多邻域环形拓扑结构提高搜索空间覆盖能力，均衡收敛速度。设置动态性能分析以及消融实验，验证算法中粒子分布质量与改进措施的有效性。采用分数阶比例-积分-微分（[FOPIλDμ]）控制下的速度伺服系统作为应用场景，将该算法与其他四种算法进行对比。结果表明，DynRing-hfpso算法较已有的元启发式优化算法有更快的收敛速度与更优的收敛精度，且在多次实验中展现出更强的鲁棒性。

关键词: 环形拓扑结构, 粒子群算法, 萤火虫算法, 分数阶PID, 自整定

Abstract: The traditional particle swarm optimization algorithm is slow to find the optimal solution and easy to fall into the local optimal solution when facing the parameter tuning problem of higher dimensions. This paper proposes a dynamic ring topolopy hybrid firefly and particle swarm optimization (DynRing-hfpso) algorithm. This algorithm takes the particle swarm algorithm as the base, incorporates advantages of firefly algorithm, enables the particles to alternate between global search and local exploration independently in the iterative process by defining the selection logic, and improves the utilization of information in the iterative process of the algorithm with an adaptive particle velocity and position constraint method. In addition, the particle swarm topology is improved to enhance the search space coverage and equalize the convergence speed with dynamic multi-neighborhood ring topology. Dynamic performance analysis and ablation experiments are set up to verify the effectiveness of the particle distribution quality and improvement measures in the algorithm. Then, a velocity servo system under fractional order proportion integration differentiation [(FOPIλDμ)] control is used as an application scenario to compare the performance of this algorithm with the other four algorithms. The results show that the DynRing-hfpso algorithm has faster convergence speed and better convergence accuracy than the existing metaheuristic optimization algorithms, and shows stronger robustness in several experiments.

Key words: ring topology, particle swarm optimization, firefly algorithm, fractional order PID (proportion integration differentiation), self-tuning

王浩丞, 李凌. 动态拓扑结构混合粒子群算法及其应用[J]. 计算机科学与探索, 2024, 18(8): 2065-2079.

WANG Haocheng, LI Ling. Hybrid Particle Swarm Algorithm with Dynamic Topology and Its Applications[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(8): 2065-2079.

参考文献

[1] KHUBALKAR S, JUNGHARE A, AWARE M, et al. Modeling and control of a permanent-magnet brushless DC motor drive using a fractional order proportional-integral-derivative controller[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2017, 25(5): 4223-4241.
[3] KARABOGA D, BASTURK B. On the performance of artificial bee colony (ABC) algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697.
[3] GOLDBERG D E. Genetic algorithms in search, optimization and machine learning[M]. [S.l.]: Addison-Wesley Longman Publishing Co., Inc., 1989.
[4] DORIGO M, STüTZLE T. The ant colony optimization metaheuristic: algorithms, applications, and advances[M]// Handbook of Metaheuristics. Berlin, Heidelberg: Springer,2003: 250-285.
[5] ZHAO W, ZHANG Z, WANG L. Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications[J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103300.
[6] ABD-ELAZIM S M, ALI E S. A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design[J]. International Journal of Electrical Power & Energy Systems, 2013, 46: 334-341.
[7] HASHIM F A, HOUSSEIN E H, HUSSAIN K, et al. Honey badger algorithm: new metaheuristic algorithm for solving optimization problems[J]. Mathematics and Computers in Simulation, 2022, 192: 84-110.
[8] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the 1995 International Conference on Neural Networks, Perth, Nov 27-Dec 1, 1995. Piscataway: IEEE, 1995: 1942-1948.
[9] ACHARYA D S, SARKAR B, BHARTI D. A fractional order particle swarm optimization for tuning fractional order PID controller for magnetic levitation plant[C]//Proceedings of the 2020 1st IEEE International Conference on Measurement, Instrumentation, Control and Automation, Kurukshetra, Jun 24-26, 2020. Piscataway: IEEE, 2020: 1-6.
[10] 王晓艳, 曹德欣. 基于进化能力的多策略粒子群优化算法[J]. 计算机工程与应用, 2023, 59(5): 78-86.
WANG X Y, CAO D X. Multi-strategy particle swarm optimization algorithm based on evolution ability[J]. Computer Engineering and Applications, 2023, 59(5): 78-86.
[11] 耿文波, 周子昂. 改进粒子群算法优化的BLDCM调速系统研究[J]. 控制工程, 2019, 26(9): 1636-1641.
GENG W B, ZHOU Z A. Research on improved particle swarm optimization for BLDCM speed control system[J]. Control Engineering of China, 2019, 26(9): 1636-1641.
[12] AYDILEK I B. A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems[J]. Applied Soft Computing, 2018, 66: 232-249.
[13] 胡涛, 蒋全. 基于改进蜜獾优化算法的PID参数整定[J]. 电子科技, 2023, 36(12): 46-54.
HU T, JIANG Q. PID parameter tuning based on improved honey badger optimization algorithm[J]. Electronic Science and Technology, 2023, 36(12): 46-54.
[14] 李丹. 改进群智能优化算法的海上物流配送路径优化方法[J]. 舰船科学技术, 2020, 42(16): 184-186.
LI D. Optimization method of marine logistics distribution path based on improved swarm intelligence optimization algorithm[J]. Ship Science and Technology, 2020, 42(16): 184-186.
[15] 王朋, 刘林, 陈哲, 等. 基于改进DE算法的PID参数整定及其应用[J]. 动力工程学报, 2015, 35(3): 191-196.
WANG P, LIU L, CHEN Z, et al. PID parameter tuning based on improved DE algorithm and the application[J].Journal of Chinese Society of Power Engineering, 2015, 35(3): 191-196.
[16] NIE J, ZHOU Y, CHEN C, et al. A PID parameter tuning method for air conditioning system based on annealing genetic algorithm[J]. Journal of Computer Applications in Technology, 2014, 50(3/4): 264-269.
[17] EKINCI S, IZCI D, HEKIMO?LU B. Optimal FOPID speed control of DC motor via opposition-based hybrid manta ray foraging optimization and simulated annealing algorithm[J].Arabian Journal for Science and Engineering, 2021, 46(2): 1395-1409.
[18] KOMMULA B N, KOTA V R. Design of MFA-PSO based fractional order PID controller for effective torque controlled BLDC motor[J]. Sustainable Energy Technologies and Assessments, 2022, 49: 101644.
[19] SHAO K, LIU J, LI M. Fractional order PID control of quadrotor UAV based on SA-PSO algorithm[C]//Proceedings of the 3rd International Conference on Computer Science and Communication Technology, Beijing, Jul 30-31, 2022: 73-81.
[20] 刘延飞, 彭征, 王艺辉, 等. 基于改进的遗传算法的有刷直流电机PID参数整定[J]. 计算机应用, 2022, 42(5): 1634-1641.
LIU Y F, PENG Z, WANG Y H, et al. PID parameter tuning of brushed direct-current motor based on improved genetic algorithm[J]. Journal of Computer Applications, 2022, 42(5): 1634-1641.
[21] 陈超波, 胡海涛, 高嵩. 人工蜂群的分数阶PID控制器参数自适应研究[J]. 控制工程, 2020, 27(6): 956-961.
CHEN C B, HU H T, GAO S. Parameters adaptive design of fractional order PID controller based on artificial bee colony algorithm[J]. Control Engineering of China, 2020, 27(6): 956-961.
[22] JAIN R V, AWARE M V, JUNGHARE A S. Tuning of fractional order PID controller using particle swarm optimization technique for DC motor speed control[C]//Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, Jul 4-6, 2017. Piscataway: IEEE, 2016: 1-4.
[23] YANG X S. Nature-inspired metaheuristic algorithms[M]. Luniver Press, 2010.
[24] 王吉权, 王福林. 萤火虫算法的改进分析及应用[J]. 计算机应用, 2014, 34(9): 2552-2556.
WANG J Q，WANG F L. Improvement analysis and application of firefly algorithm[J]. Journal of Computer Applications, 2014, 34(9): 2552-2556.
[25] YANG X S. Firefly algorithms for multimodal optimization[C]//Proceedings of the 5th International Symposium on Stochastic Algorithms: Foundations and Applications, Sapporo, Oct 26-28, 2009. Berlin, Heidelberg: Springer, 2009: 169-178.
[26] 孙晓娇, 侯旭东, 刘长良. 基于改进粒子群算法的水轮机调速器参数优化[J]. 自动化与仪表, 2010, 25(5): 26-30.
SUN X J, HOU X D, LIU C L. Optimization of hydro turbine governor parameters based on improved particle swarm optimization[J]. Automation and Instrumentation, 2010, 25(5): 26-30.
[27] 戴丽, 罗廷芳, 李杨, 等. 基于改进细菌菌落优化算法的 PID参数整定[J]. 计算机工程与应用, 2019, 55(24): 241-246.
DAI L, LUO T F, LI Y, et al. PID parameter tuning based on improved bacterial colony optimization algorithm[J]. Computer Engineering and Applications, 2019, 55(24): 241-246.
[28] RODRíGUEZ-ABREO O, RODRíGUEZ-RESéNDIZ J, FUENTES-SILVA C, et al. Self-tuning neural network PID with dynamic response control[J]. IEEE Access, 2021, 9: 65206-65215.
[29] LIU X. Optimization design on fractional order PID controller based on adaptive particle swarm optimization algorithm[J]. Nonlinear Dynamics, 2016, 84(1): 379-386.

[1]	罗逸轩, 刘建华, 胡任远, 张冬阳, 卜冠南. 融合经验共享Q学习的粒子群优化算法[J]. 计算机科学与探索, 2022, 16(9): 2151-2162.
[2]	李成严, 宋月, 马金涛. 模糊云资源调度问题的RIOPSO算法[J]. 计算机科学与探索, 2021, 15(8): 1534-1545.
[3]	纪伟, 李英梅, 季伟东, 张珑. 粒子置换的双种群综合学习PSO算法[J]. 计算机科学与探索, 2021, 15(4): 766-776.
[4]	张其文，尉雅晨. 独立自适应调整参数的粒子群优化算法[J]. 计算机科学与探索, 2020, 14(4): 637-648.
[5]	程天艺，王亚刚，龙旭，潘晓英. 多层次降维的头颈癌图像特征选择方法[J]. 计算机科学与探索, 2020, 14(4): 669-679.
[6]	王金杰，李炜. 混合互信息和粒子群算法的多目标特征选择方法[J]. 计算机科学与探索, 2020, 14(1): 83-95.
[7]	王丽娟，丁世飞. 一种粒子群优化的SVM-ELM模型[J]. 计算机科学与探索, 2019, 13(4): 657-665.
[8]	袁罗，葛洪伟，姜道银. 基于健康度的自适应过滤粒子群算法[J]. 计算机科学与探索, 2018, 12(2): 332-340.
[9]	许欧阳，李光辉. 萤火虫优化和随机森林的WSN异常数据检测[J]. 计算机科学与探索, 2018, 12(10): 1633-1644.
[10]	万逸君，张大坤，郑亚振. 三维片上网络离散量子粒子群布图算法研究[J]. 计算机科学与探索, 2017, 11(12): 1953-1964.
[11]	曹黎侠，黄光球. 基于粒子群算法的粗糙博弈模型与算法设计[J]. 计算机科学与探索, 2016, 10(4): 565-572.
[12]	陆克中，孙俊. 萤火虫算法收敛分析[J]. 计算机科学与探索, 2016, 10(2): 293-300.
[13]	张成兴. 压缩因子综合信息粒子群算法[J]. 计算机科学与探索, 2014, 8(4): 506-512.
[14]	徐开阔1 ,唐常杰1+ ,刘胤田2 ,张天庆1 ,段磊1 . 基于聚类排序选择方法的进化算法[J]. 计算机科学与探索, 2008, 2(3): 321-329.