A Hybrid Particle Swarm Optimization Algorithm Based on Immune Selection for Stochastic Loader Problem

Hong Wang; Pei Yi Zhao

doi:10.4028/www.scientific.net/AMM.321-324.2214

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324A Hybrid Particle Swarm Optimization Algorithm...

A Hybrid Particle Swarm Optimization Algorithm Based on Immune Selection for Stochastic Loader Problem

Abstract:

The Particle Swarm Optimization Algorithm is a new computational method for the combinatorial optimization problem, it is simple and effective, but it does suffer from the premature convergence. For overcome this problem and finding the optimal solution of the Stochastic Loader problem, we presented a new hybrid Particle Swarm Optimization Algorithm that combines with Artificial Immune Algorithm, such as immune memory, antibody promotion and suppression, immune selection and so on. Numerical example illustrates the higher efficiency and reliability of the new hybrid PSO compared with the basic Particle Swarm Optimization Algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

2214-2218

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.2214

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Online since:

June 2013

Authors:

Hong Wang, Pei Yi Zhao

Keywords:

Artificial Immune Algorithm, Particle Swarm Optimization (PSO), Stochastic Loader Problem

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References

[1] Guochun Tang: Operations Research and Management Science, vol.14, No.4, pp.15-18, 2005. In Chinese.

Google Scholar

[2] Guochun Tang: Journal of Shanghai Second Polytechnic University, vol.22, No.1, pp.1-5, 2005. In Chinese.

Google Scholar

[3] Baoding Liu, Ruiqing Zhao, Gang Wang: Tsinghua University press, 2003. In Chinese.

Google Scholar

[4] Kennedy J, Eberhart R C: Particle swarm Optimization. Proc. of IEEE International Conference on Neural Networks. 1995(4): 1942~ 1948.

Google Scholar

[5] Clerc M: Discrete partice swarm optimization illustrated by the traveling salesman problem .2000.http: // www.ma-uriceclerc.net.

Google Scholar

[6] Peram T, Veeramachaneni K, Mohan C K: Fitness-distance-ratio based particle swarm optimization. Proc. of Swarm Intelligence Symposium. 2003, 174~181.

DOI: 10.1109/sis.2003.1202264

Google Scholar

[7] Parsopoulos K E, Vrahatis M N: UPSO-A united particle swarm optimization scheme. Lecture Series on Computational Sciences. 2004，868~873.

DOI: 10.1201/9780429081385-222

Google Scholar

[8] Liang J J, Qin A K，Suganthan P N, et al: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions .IEEE Transactions on Evolutionary Computation. 2006，10:281~296.

DOI: 10.1109/tevc.2005.857610

Google Scholar

[9] Jianxiang Wei, Yuehong Sun, Xinning Su: Journal of Nanjing University, 2010，46(1): 1~8. In Chinese.

Google Scholar

[10] Guolian Liu, Guanzheng Tan: computer simulation.2008，25(7),162~165. In Chinese.

Google Scholar