A Inflexion Nonlinear Global Particle Swarm Optimization (PSO) Algorithm

Abstract:

According to the question of the standard particle swarm optimization (PSO) algorithm is prone to premature and no convergence phenomenon, this paper proposed an algorithm of Inflection nonlinear global PSO. The algorithm introduces nonlinear trigonometric factor and the global average location information in the formula of velocity updating. It take advantage of the convex of the triangle function cause the particles early in the larger velocity search maintain long time and in the later searching with smaller speed maintain long time, use the global average position information make the population can use more information to update their position. The method are applied in optimizing in the parameters of the main steam temperature control system and furnace pressure control system for comparison, the results show that the method in the search speed and precision than standard PSO has significantly improved.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 1582-1585
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.1582
• Online since: July 2014
• Authors: Li Xia Lv*, Xiang Yu Lin
• Keywords: Global Average Location, Inflection Nonlinear, Premature, Standard Particle Swarm Optimization Algorithm

References

[1] Xugang, Qu Jin-ping. An improved adaptive particle swarm optimization algorithm [J]. South China University of Technology: Natural Science Edition, 2008, 36 (9): 6-10. (In Chinese).

[2] Zhang jie, Pei fang. Multi-strategy cooperative particle swarm optimization algorithm [J]. Computer Applications Research . 2013. 30 (10): 2965 -2967. (In Chinese).

[3] Shao peng, Wu zhi-jian. Rosenbrock Function Optimization Based on Improved Particle Swarm Optimization Algorithm [J]. Computer science. 2013. 40(9): 194-197. (In Chinese).

[4] Su Jin-rong, Li Bing-yi, Wang Xiao-kai. Particle swarm optimization using average information of swarm[J]. Computer Engineering and Applications, 2007, 43(10): 58-59．(In Chinese).

[5] Xu gang, Yang Yu-qun, Liu Bin-bin. A Nonlinear Adaptive Particle Swarm Optimization[J]. Journal of Nanchang University(Natural Science). 2011, 35(4) 323-326. (In Chinese).

[6] SHI Y, EBERHART R, C EMPIRICAL. Study of particle swarm optimizer[C]. Proceeding of the World Muhi conference on Syatemics. Cybernetics and Informatics, Orlando, (2000).

[7] Huang Ze-xia, Yu You-hong, Huang De-cai. Quantum particle swarm optimization algorithm of adaptive adjustment inertia weights [J]. Journal of Shanghai Jiaotong University. 2012, 46(2): 228 -232. (In Chinese).