Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (04): 78-83.doi: 10.12052/gdutxb.160079

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An Improved Particle Swarm Algorithm Far Away from Worst Solution

Liu Wen-kai, Wen Jie-chang   

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2016-06-12 Online:2017-07-09 Published:2017-07-09

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Abstract:

A new particle swarm optimization (PSO) algorithm is proposed in reference to worst solution of being local in standard PSO to avoid the problems of being easy to converge to a local minimum, premature convergence and low precision. In the late period of particle swarm optimization and global optimum (Gbest) when there is no change, the local optimum (Pbest) is equal to the Gbest. Then the speed to move closer to the optimal direction vector is zero, and particles go along the direction only by its own inertia. When the proposed algorithm is added to the local minimum (Pworst) algorithm, the particle's forward move is not only affected by its own inertia, but it also can continue to find the best, so as to find the global optimal. This algorithm, by applying the idea of the particles avoiding the worst local and global solution, improves the velocity updating formula based on the PSO, and tests respectively the worst global solution and local worst solution on the influence of particle swarm optimization algorithm. Several typical test function simulations show that the proposed algorithm not only has great advantages of convergence property over some other modified PSO algorithms, but is also effective in avoiding being trapped in local optimal solution.

Key words: particle swarm optimization (PSO), global worst

CLC Number: 

  • TP301

[1] KENNEDY J, EBERHART R C. Particle swarm optimization [J]. Proc IEEE Int Conf Neural Networks, 1995, 4(8): 1942-1948.
[2] SCHMITT M, WANKA R. Particle swarm optimization almost surely finds local optima[J]. Theoretical Computer Science, 2014, 561: 57-72.
[3] SHI Y, EBERHART R. Modified particle swarm optimizer [C]// IEEE International Conference on Evolutionary Computation Proceedings. Anchorage, Alaska, USA: IEEE Service Center. 1998: 69-73.
[4] 赵志刚, 黄树运, 王伟倩. 基于随机惯性权重的简化粒子群优化算法[J]. 计算机应用研究, 2014, 31(2): 361-363.ZHAO Z G, HUANG S Y, WANG W Q. Simplified particle swarm optimization algorithm based on stochastic inertia weight [J]. Application Research of Computers, 2014, 31(2): 361-363.
[5] 张选平, 杜玉平, 秦国强, 等. 一种动态改变惯性权的自适应粒子群算法[J]. 西安交通大学学报, 2005, 39(10): 1039-1042.ZHANG X P, DU Y P, QIN G Q, et al. Adaptive particle swarm algorithm with dynamically changing inertia weight [J]. Journal of Xi’an Jiaotong University, 2005, 39(10): 1039-1042.
[6] RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 240-255.
[7] P N SUGANTHAN. Particle swarm optimizer with neighborhood operator [C]// Evolutionary Computation. Washington DC, US: Procc IEEE Congr Evol Comput. 1999: 1958-1962.
[8] 任凤鸣, 李丽娟. 改进的PSO算法中学习因子(c1, c2)取值的实验与分析[J]. 广东工业大学学报, 2008, 25(1): 86-89.REN F M, LI L J. Experiment and analysis of the value selection of acceleration coefficients (c1, c2) in PSO method[J]. Journal of Guangdong University of Technology, 2008, 25(1): 86-89.
[9] KENNEDY J, MENDES R. Population structure and particle swarm performance [C]// Evolutionary Computation, CEC’02. Hawaii, US: Congress on Evolutionary Computation. 2002: 1671-1676.
[10] KENNEDY J, MENDES R. Neighborhood topologies in fully informed and best-of-neighborhood particle swarms [J]. IEEE Transactions on Systems Man & Cybernetics Part C, 2006, 36(4): 515-519.
[11] 马胜蓝, 叶东毅, 杨玲玲. 一种新的粒子群拓扑设计准则[J]. 计算机工程, 2015, 41(1): 200-206.MA S L, YE D Y, YANG L L. A new design criteria of particle swarm topology[J]. Computer Engineering, 2015, 41(1): 200-206.
[12] 石松, 陈云. 层次环形拓扑结构的动态粒子群算法[J]. 计算机工程与应用, 2013, 49(8): 1-5.SHI S, CHEN Y. Dynamic particle swarm optimization algorithm with hierarchical ring topology [J]. Computer Engineering and Applications, 2013, 49(8): 1-5.
[13] 方峻, 唐普英, 任诚. 一种基于加权有向拓扑的改进粒子群算法[J]. 计算机技术与发展, 2006, 16(8): 62-65.FANG J, TANG P Y, REN C. A modified particle swarm optimization based on directional weighting Topology [J]. Computer Technology and Development, 2006, 16(8): 62-65.
[14] CHEN Y, PENG W C, JIAN M C. Particle swarm optimization with recombination and dynamic linkage discovery[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2007, 37(6): 1460-1470.
[15] ANDREWS P S. An investigation into mutation operators for particle swarm optimization [C]// Evolutionary Computation. Vancouver, Canada: IEEE Congress on Evolutionary Computation. 2006: 1044-1051.
[16] 张创业, 莫愿斌. 基于协同进化思想的人工鱼和粒子群混合优化算法[J]. 广西民族大学学报: 自然科学版, 2009, 15(1): 74-77.ZHANG C Y, MO Y B. AFSA and PSO hybrid algorithm based on collaborative evolution [J]. Journal of Guangxi University for Nationalities (Natural Science Edition). 2009, 15(1): 74-77.
[17] 周玉光, 曾碧, 叶林锋. 改进粒子群优化算法及其在4G网络基站选址中的应用[J]. 广东工业大学学报, 2015, 32(2): 64-68.ZHOU Y G, ZENG B, YE L F. Improved particle swarm optimization and its application in 4G network base station location [J]. Journal of Guangdong University of Technology, 2015, 32(2): 64-68.
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