An Improved Particle Swarm Optimization

Yi Ni Wang

doi:10.4028/www.scientific.net/AMM.380-384.1542

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384An Improved Particle Swarm Optimization

An Improved Particle Swarm Optimization

Abstract:

By analyzing the current Particle Swarm Optimization, especially the analysis of weighting coefficient descending and random disturbance term improving, an improved Particle Swarm Optimization combining periodic weighting adjustment and random disturbance was put forward and its effectiveness is verified by experiments in this article.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

1542-1545

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.1542

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Online since:

August 2013

Authors:

Yi Ni Wang

Keywords:

Improve, Particle Swarm Optimization (PSO), Random Disturbance, Weight

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References

[1] Quan Furong, The Theoretical Analysis and Research on the Particle Swarm Optimization , Silicon Valley, vol. 23, pp.31-56, (2010).

Google Scholar

[2] Parsopoulos K.E., Vrahatis M. N, Particle Swarm Optimization Method for Constrained Optimization Problems, Proceedings of the Euro-International Symposium on Computational Intelligence, vol. 34, May. 2002, pp.62-78.

DOI: 10.1145/508791.508907

Google Scholar

[3] Ji Zhen, Liao Huilian, Wu Qinghua, The Particle Swarm Optimization [M]. Science Press, (2009).

Google Scholar

[4] Jiang M, Luo Y P, Yang S Y, Stochastic Convergence Analysis and Parameter Selection of the Standard Particle Swarm Optimization Algorithm, Information Processing Letters, vol. 102, Jan. 2007, pp.8-16.

DOI: 10.1016/j.ipl.2006.10.005

Google Scholar

[5] Natsuki H, Particle Swarm Optimization with Gaussian Mutation, Proc of the Congress on Evolutionary Computation, Indianapolis, Indiana, pp.72-79, (2003).

Google Scholar

[6] Shi Y, Eberhart R, A Modified Particle Swarm Optimizer, IEEE World Congress on Computational Intelligence. Piscataway: IEEE Press, pp.69-73, (1998).

DOI: 10.1109/icec.1998.699146

Google Scholar

[7] Yuan Lin, Yuan Weiwei, An Improved Particle Swarm Optimization, [J]. Journal of Shenyang Ligong University, vol. 31, Mar. 2012, pp.15-18.

Google Scholar

[8] He Qingyuan, Han Chuanjiu. An Improved Particle Swarm Optimization with Disturbances, Computer Engineering and Application, vol. 43, Jul. 2007, pp.84-86.

Google Scholar