An Improved PSO Based on Diversity of Particle Symmetrical Distribution
SUN Yue-Hong1,2,WEI Jian-Xiang3,XIA De-Shen1
1.School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 2.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210046 3.Department of Information Science,Nanjing College for Population Programme Management,Nanjing 210042
Abstract Particle swarm optimization (PSO) is easy to fall into the local optimum as the diversity of population gets worse and worse during the evolution. To overcome the shortcoming, an improved PSO based on the diversity of particle symmetrical distribution (sdPSO) is developed. Over the research of the spatial distribution of particles, it can be found that the convergence probability to the global optimum solution is greatly improved with more symmetrical particle distribution surrounding the optimum solution of particles. A diversity population function is proposed and an adjustment algorithm for the diversity is introduced into the basic PSO. The spatial distribution of particles varies between asymmetry and symmetry repeatedly while the population diversity is adjusted continually, which make the improved algorithm search in a wider range. The simulation results show that the improved sdPSO algorithm achieves better convergence precision than the basic PSO by the experiment of benchmark functions.
[1] Kennedy J, Eberhart R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, Australia, 1995, Ⅳ: 1942-1948 [2] Zhang Wen, Liu Yutian. Adaptive Particle Swarm Optimization for Reactive Power and Voltage Control in Power Systems // Proc of the 1st International Conference on Advances in Natural Computation. Changsha, China, 2005: 449-452 [3] Jin Yixiong, Cheng Haozhong, Yan Jianyong, et al. Improved Particle Swarm Optimization Method and Its Application in Power Transmission Network Planning. Proc of the CSEE, 2005, 25(4): 46-50,70 [4] Du Tianjun, Chen Guangju, Liu Zhanchen. A Network and Its Simulation for Trajectory Recognition Based on PSO Algorithm. Journal of System Simulation, 2004, 16(11): 2517-2519 (in Chinese) (杜天军,陈光礻禹,刘占辰.基于PSO算法的弹道辨识网络及仿真.系统仿真学报, 2004, 16(11): 2517-2519) [5] Marco M, Selleri S, Pirinoli P, et al. Improved Particle Swarm Optimization Algorithms for Electromagnetic Optimization. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, 2008, 19(1): 75-84 [6] Hu Chunxia, Zeng Jianchao. Immune Particle Swarm Optimization Based on Sharing Mechanism. Journal of System Simulation, 2008, 20(16): 4278-4280,4285 (in Chinese) (胡春霞,曾建潮.共享免疫微粒群算法.系统仿真学报, 2008, 20(16): 4278-4280,4285) [7] Zhang Xuanping, Du Yuping, Qin Guoqiang, et al. Adaptive Particle Swarm Algorithm with Dynamically Changing Inertia Weight. Journal of Xian Jiaotong University, 2005, 39(10): 1039-1042 (in Chinese) (张选平,杜玉平,秦国强,等.一种动态改变惯性权重的自适应粒子群算法.西安交通大学学报, 2005, 39(10): 1039-1042) [8] He Ran, Wang Yongji, Wang Qing, et al. An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity. Journal of Software, 2005, 16(12): 2036-2044 (in Chinese) (赫 然,王永吉,王 青,等.一种改进的自适应逃逸微粒群算法及实验分析.软件学报, 2005, 16(12): 2036-2044) [9] Lü Zhensu, Hou Zhirong. Particle Swarm Optimization with Adaptive Mutation. Acta Electronica Sinica, 2004, 32(3): 416-420 (in Chinese) (吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报, 2004, 32(3): 416-420) [10] Lü Yanping, Li Shaozi, Chen Shuili, et al. Particle Swarm Optimization Based on Adaptive Diffusion and Hybrid Mutation. Journal of Software, 2007, 18(11): 2740-2751 (in Chinese) (吕艳萍,李绍滋,陈水利,等.自适应扩散混合变异机制微粒群算法.软件学报, 2007, 18(11): 2740-2751) [11] Hu Wang, Li Zhishu. A Simpler and More Effective Particle Swarm Optimization Algorithm. Journal of Software, 2007, 18(4): 861-868 (in Chinese) (胡 旺,李志蜀.一种更简化而高效的粒子群优化算法.软件学报, 2007, 18(4): 861-868) [12] Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. Hybrid Particle Swarm Optimizer with Mass Extinction // Proc of the International Conference on Communication, Circuits and Systems. Chengdu, China, 2002: 1170-1173 [13] Lovbjerg M, Krink T. Extending Particle Swarm Optimizers with Self-Organized Criticality // Proc of the Congress on Evolutionary Computation. Honolulu, USA, 2002: 1588-1593 [14] Silva A, Neves A, Costa E. An Empirical Comparison of Particle Swarm and Predator Prey Optimization // Proc of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science. Limerick, Ireland, 2002: 103-110 [15] Duan Xiaodong, Gao Hongxia, Zhang Xuedong, et al. Relations between Population Structure and Diversity of Particle Swarm Optimization Algorithm. Computer Science, 2007, 37(11): 164-166,177 (in Chinese) (段晓东,高红霞,张学东,等.粒子群算法种群结构与种群多样性的关系研究.计算机科学, 2007, 37(11): 164-166,177) [16] Ursem R K. Diversity-Guided Evolutionary Algorithms // Proc of the 7th International Conference on Parallel Problem Solving from Nature. Granada, Spain, 2002: 462-474 [17] Jie Jing, Zeng Jianchao, Han Chongzhao. Self-Organized Particle Swarm Optimization Based on Feedback Control of Diversity. Journal of Computer Research and Development, 2008, 45(3): 467-471 (in Chinese) (介 婧,曾建潮,韩崇昭.基于群体多样性反馈控制的自组织微粒群算法.计算机研究与发展, 2008, 45(3): 467- 471) [18]Shi Y, Eberhart R C. A Modified Particle Swarm Optimizer // Proc of the IEEE International Conference of Evolutionary Computation. Anchorage, USA, 1998: 69-73
2010, Vol. 23 
