An Adaptive Particle Swarm Optimization Algorithm Based on Cloud Model

Jin Rong Zhu

doi:10.4028/www.scientific.net/AMR.129-131.612

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 129-131An Adaptive Particle Swarm Optimization Algorithm...

An Adaptive Particle Swarm Optimization Algorithm Based on Cloud Model

Abstract:

In this paper, an adaptive particle swarm optimization algorithm based on cloud model (C-APSO) is proposed. In the suggested method, the velocities of the all particles are adjusted based on the strategy that a particle whose fitness value is nearer to the optimal particle will fly with smaller velocity. Considering the properties of randomness and stable tendency of a normal cloud model, a Y-conditional normal cloud generator is used to gain the inertial factors of the particles. The simulations of function optimization show that the proposed method has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

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Periodical:

Advanced Materials Research (Volumes 129-131)

Pages:

612-616

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.129-131.612

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Online since:

August 2010

Authors:

Jin Rong Zhu

Keywords:

Adaptive Particle Swarm Optimization, Cloud Model, Particle Swarm Optimization Algorithm (PSO)

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References

[1] Kennedy J, Eberhart R C, in: Proc of IEEE Int Conf on Neural Networks. Piscataway, NJ: IEEE Press (1995), p. (1942).

Google Scholar

[2] Eberhart R C, Shi Y H. , in: Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway, USA: IEEE Service Center (2001), p.81.

Google Scholar

[3] Shi Y, Eberhart R C, in: IEEE International Conference of Evolutionary Computation, Anchorage, Alaska, May (1998), p.69.

Google Scholar

[4] Shi Y, Eberhart R C., in: Proceedings of the Congress on Evolutionary Computation. Seoul, Korea (2001), p.101.

Google Scholar

[5] Li Yonggang, Gui Weihua, Yang Chunhua, Chen Zhishen: Control and Decision, Vol. 23, No. 1 (2008), p.95.

Google Scholar

[6] Lu Zhensu, Hou Zhirong: ACTA Electronica Sinica. Vol. 32, No. 3 (2004), p.416.

Google Scholar

[7] Li Deyi, Meng Haijun, Shi Xuemei: Journal of Computer Research and Development, Vol. 32, No. 6(1995),P. 15.

Google Scholar

[8] Hongfei Lu, Erxu Pi, Qiufa Peng, Lanlan Wang and Changjiang Zhang: Expert Systems with Applications, Vol. 36 (2009), p.9388.

Google Scholar

[9] Dai chao-hua, Zhu Yunfang, Chen Weirong, Lin Jianhui: ACTA Electronica Sinica, Vol. 35 (2007), p.1419.

Google Scholar

[10] Zhu Yunfang, Dai chao-hui, Chen Weirong, Lin Jianhui: Journal of Computational Information System, Vol. 1 (2005), p.203.

Google Scholar

[11] Li Xingsheng: Study on classification and clustering mining based on cloud model and data field. PLA University of Science and Technology (2003).

Google Scholar

10 20 30 40 50 60 70 80 90 100 -70 -60 -50 -40 -30 -20 -10.

Google Scholar

[10] Number of Iterations In(AFV) PSO APSO C-APSO.

Google Scholar

10 20 30 40 50 60 70 80 90 100.

Google Scholar

[20] [40] [60] [80] 100 Number of Iterations Average Fitness Values PSO APSO C-APSO.

Google Scholar

50 100 150 200 250 300 350 400 450 500.

Google Scholar

[5] [10] [15] [20] Number of Iterations In(AFV) PSO APSO C-APSO.

Google Scholar