Hybrid quantum particle swarm optimization algorithm for combinatorial optimization problem

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Hybrid quantum particle swarm optimization algorithm for combinatorial optimization problem

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents

London, England

POSTER SESSION: Ant colony optimization, swarm intelligence, and artificial immune systems: posters table of contents

Pages: 185 - 185

Year of Publication: 2007

ISBN:978-1-59593-697-4

Authors

Jiahai Wang	Sun Yat-sen University, Guangzhou, China
Yanlan Zhou	Sun Yat-sen University, Guangzhou, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

In this paper, a framework of hybrid PSO is proposed by reasonablycombining the Q-bit evolutionary search of quantum PSO and binary bit evolutionary search of genetic PSO.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	J. Kennedy, R.C. Eberhart. A discrete binary version of the particle swarm algorithm. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics. NJ: Piscatawary, 4104--4109, 1997.
	2	S.Y. Yang, M. Wang, L.C. Jiao. A quantum particle swarm optimization. Proceeding of the 2004 IEEE Congress on Evolutionary Computation, 1:320--324, 2004.
	3	P.Y. Yin. Genetic particle swarm optimization for polygonal approximation of digital curves. Pattern Recognition and Image Analysis, 16(2):223--233, 2006.
	4	David Pisinger, Where are the hard knapsack problems?, Computers and Operations Research, v.32 n.9, p.2271-2284, September 2005 [doi>10.1016/j.cor.2004.03.002]