An Improved Particle Swarm Optimization Based on Biological Chemotaxis

Hui Xia

doi:10.4028/www.scientific.net/AMR.1015.737

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An Improved Particle Swarm Optimization Based on Biological Chemotaxis
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1015An Improved Particle Swarm Optimization Based on...

An Improved Particle Swarm Optimization Based on Biological Chemotaxis

Abstract:

Standard particle swarm algorithm for function optimization prone to local optimal and premature convergence, and thus the biological chemotaxis principle introduction to particle swarm optimization algorithm, this paper proposed an improved algorithm to maintain the diversity of the populationand the choice of key parameters. Simulation results show that, compared with the traditional particle swarm optimization algorithm, an improved particle swarm algorithm for dealing with complex multimodal function optimization problem can be significantly improved algorithm for global optimization.

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Periodical:

Advanced Materials Research (Volume 1015)

Pages:

737-740

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1015.737

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Online since:

August 2014

Authors:

Hui Xia*

Keywords:

Biological Chemotaxis, Exclusion Operation, PSO, Standard Test Function

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* - Corresponding Author

References

[1] Kennedy J , Eberhart R. Particle swarm optimization [ C ]/ IEEE International Conference on Neural Networks.

Google Scholar

[2] SHI Y, Eerhart R C. Fuzzy adaptive particle swarm optimization [ C]/ IEEE International Conference on Evolutionary Computation. Piscataway , NJ : IEEE Press , (2001).

Google Scholar

[3] Ciuprina G, Ioan D , Munteanu I. Use of intelligent2particle swarm optimization in elect romagnetics[J ] . IEEE Trans on Magnetics , 2002 , 38 (2) : 1037-1040.

DOI: 10.1109/20.996266

Google Scholar

[4] Clere M , Kennedy J . The particle swarm2explosion , stability , and convergence in a multidimensional complex space[J ] . IEEE Trans on Evolutionary Computation , 2002 , 6 (1) : 58-73.

DOI: 10.1109/4235.985692

Google Scholar

[5] Breaban M , Luchian H. PSO under an adaptive scheme [ C]/ The 2005 IEEE Congress on Evolutionary Computation. Piscataway , NJ : IEEE Press , (2005).

DOI: 10.1109/cec.2005.1554828

Google Scholar

[6] Robinson J , Yahya R S. Particle swarm optimization in elect romagnetics [J ] . IEEE Trans on Antennas and Propagation , 2004 , 52 (2) : 397-407.

Google Scholar

[7] SHI Y, Eberhart R. A modified particle swarm optimizer [ C ]/ IEEE International Conference on Evolutionary Computation. Piscataway , NJ : IEEE Press , l998.

DOI: 10.1109/icec.1998.699146

Google Scholar

[8] L0vbjerg M , Rasmussen T K, Krink T. Hybrid particle swarm optimizer with breeding and subpopulations [ C]/ The Third Genetic and Evolutionary Computation Conference. San Francisco , CA : Morgan Kaufmann Press , (2001).

Google Scholar