A New Improved Adaptive Hybrid Particle Swarm Optimization Algorithm

Xiang Tian; Yue Lin Gao

doi:10.4028/www.scientific.net/AMM.427-429.1710

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429A New Improved Adaptive Hybrid Particle Swarm...

A New Improved Adaptive Hybrid Particle Swarm Optimization Algorithm

Abstract:

This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and aims at the shortcoming of PSO algorithm, which is easily plunging into the local minimum, then we proposes a new improved adaptive hybrid particle swarm optimization algorithm. It adopts dynamically changing inertia weight and variable learning factors, which is based on the mechanism of natural selection. The numerical results of classical functions illustrate that this hybrid algorithm improves global searching ability and the success rate.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

1710-1713

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.1710

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Online since:

September 2013

Authors:

Xiang Tian*, Yue Lin Gao

Keywords:

Natural Selection, Particle Swarm Optimization (PSO), Self-Adaptive Inertia Weight, Variable Learning Factors

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References

[1] J. kennedy,R. Eberhart, in: Particle Swarm Optimization[J]. IEEE, pp.1942-1948 (1995).

Google Scholar

[2] I. Mazhoud, in: Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism[J]. Eng. Appl. Artif. Intel. pp.1-11 (2013).

Google Scholar

[3] Y. Shi, in: Empirical study of particle swarm optimization[A]. /International Conference on Evolutionary Compution[C]. Washington, USA: IEEE, pp.1945-1950 (1999).

Google Scholar

[4] Y. Shi,R. Eberhart, in: Fuzzy adaptive particle swarm optimization[A]. The IEEE Congress on Evolutionary Compution[C], San Francisco, USA: IEEE, pp.101-106(2001).

DOI: 10.1109/cec.2001.934377

Google Scholar

[5] Riccardo Poli, in: Particle swarm optimization-An overview[J]. Swarm Intell. P. 33-57(2007).

DOI: 10.1007/s11721-007-0002-0

Google Scholar

[6] Zhao X, in: A hybrid particle swarm optimization approach for design of agri-food supply chain network[J]. IEEE on Network, pp.162-167(2011).

DOI: 10.1109/soli.2011.5986548

Google Scholar