An Improved Particle Swarm Optimization Algorithm Containing Resident Particles

doi:10.3969/j.issn.1006-2475.2017.11.001

Abstract

Abstract: An improved particle swarm optimization algorithm (CRPSO) is proposed to improve the premature convergence of particle swarm optimization algorithm (PSO). The particles of basic PSO are called as main particles in the improved algorithm. When the improved algorithm finds a better globally optimal extreme value point, it produces several points named resident particles around the global optimization point. The two kinds of particles work in cooperation that main particles are responsible for global research and resident particles for local search. Resident particles will help main particles to avoid falling into local extremum easily and improve the diversity of the whole particle swarm. The simulation results have validated its feasibility and effectiveness.

Key words: PSO, premature convergence, main particles, resident particles

CLC Number:

TP18

ZHANG Hong-wei, ZHANG Xiang-feng, ZHU Chen-xuan. An Improved Particle Swarm Optimization Algorithm Containing Resident Particles[J]. Computer and Modernization, 2017, 0(11): 1-5+12.

References

[1] Kennedy J, Eberhart R C. Particle swarm optimization[C]// Proceedings of the 1995 IEEE International Conference on Neural Networks. 1995,4:1942-1948.

[2] 迟玉红,孙富春,王维军,等. 基于空间缩放和吸引子的粒子群优化算法[J]. 计算机学报, 2011,34(1):115-130.

[3] Kuo R J, Hong S Y, Huang Y C. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection[J]. Applied Mathematical Modelling, 2010,34(12):3976-3990.

[4] Zhang Wen, Liu Yutian. Adaptive particle swarm optimization for reactive power and voltage control in power systems[C]// Proceedings of the 1st International Conference on Advances in Natural Computation. 2005,3:449-452.

[5] 杨毅,雷霞,徐贵阳,等. 采用PSO-BF算法的微电网多目标电能优化调度[J]. 电力系统保护与控制, 2014,42(13):13-20.

[6] 殷丽娟,赵熙临,梅真. 基于混沌粒子群优化算法的微电网优化运行技术[J]. 电力系统及其自动化学报, 2016,28(5):55-61.

[7] 戴文智,杨新乐. 基于惯性权重对数递减的粒子群优化算法[J]. 计算机工程与应用, 2015,51(17):14-19.

[8] Sun Shiquan, Peng Qinke. A hybrid PSO-GSA strategy for high-dimensional optimization and microarray data clustering[C]// Proceedings of the 2014 IEEE International Conference on Information and Automation. 2014:41-46.

[9] 邵鹏,吴志健. 一种带正弦函数因子的粒子群优化算法[J]. 小型微型计算机系统, 2015,36(1):156-161.

[10] Peer E S, Van den Bergh F, Engelbrecht A P. Using neighbourhoods with the guaranteed convergence PSO[C]// Proceedings of the 2003 IEEE Swarm Intelligence Symposium. 2003:235-242.

[11] 孙越泓,魏建香,夏德深. 一种基于粒子对称分布多样性的PSO算法[J]. 模式识别与人工智能, 2010,23(2):137-143.

[12] 谭阳,唐德权,全惠云. 一种排异竞争的粒子群优化算法[J]. 系统仿真学报, 2011,23(12):2635-2640.

[13] Angeline P J. Using selection to improve particle swarm optimization[C]// Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. 1998:84-89.

[14] Higashi H, Iba H. Particle swarm optimization with Gaussian mutation[C]// Proceedings of the 2003 IEEE Swarm Intelligence Symposium. 2003:72-79.

[15] 魏建香,孙越泓,苏新宁. 一种基于免疫选择的粒子群优化算法[J]. 南京大学学报(自然科学版), 2010,46(1):1-9.

[16] 彭力,王茂海. 基于前馈扰动的粒子群改进算法[J]. 控制工程, 2012,19(1):102-105.

[17] 匡君芳,金忠,徐蔚鸿,等. Tent混沌人工蜂群与粒子群混合算法[J]. 控制与决策, 2015,30(5):839-847.