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Reseach Article

Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization

by Deepak Singh, Vikas Singh, Uzma Ansari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 11
Year of Publication: 2011
Authors: Deepak Singh, Vikas Singh, Uzma Ansari
10.5120/3428-4281

Deepak Singh, Vikas Singh, Uzma Ansari . Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization. International Journal of Computer Applications. 28, 11 ( August 2011), 19-24. DOI=10.5120/3428-4281

@article{ 10.5120/3428-4281,
author = { Deepak Singh, Vikas Singh, Uzma Ansari },
title = { Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 11 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number11/3428-4281/ },
doi = { 10.5120/3428-4281 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:31.164506+05:30
%A Deepak Singh
%A Vikas Singh
%A Uzma Ansari
%T Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 11
%P 19-24
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The field of discrete optimization consists of the areas of linear and integer programming, cover problems, knapsack problems, graph theory, network-flow problems, and scheduling. This paper performs an Experiment for discrete Optimization problem with the Hybridization of Binary Particle Swarm Optimization (BPSO) and Genetic Crossover. There are many algorithms Present for solving discrete optimization problem. Both BPSO and GA have shown to be very effective results. Experiment performed on this paper is for the analysis and behavioral study of Hybridized algorithm. We conclude with the results obtained by the performed experiment on standard benchmark functions, and it is found that proposed algorithm gives better results for few standard benchmark functions.

References
  1. J. Kennedy and R. Eberhart, Particle swarm optimization, in Proc. IEEE International Conference Neural Networks, vol. 4, 1995, pp. 1942 - 1948..
  2. Y. Shi and R. C. Eberhart, A modified particle swarm optimizer, in Proc. IEEE International Conference on Evolutionary Computation, Piscataway,NJ, 1998, IEEE Press, pp. 69-73.
  3. R. C. Eberhart and Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, 2000 Congress on Evolutionary Computing, vol. 1, 2000, pp. 84-88.
  4. Zhi-Feng Hao, Zhi-Gang Wang and Han Huang, A Particle swarm optimization algorithm with crossover operator, in Proc. of the Sixth International Conference on Machine Learning and Cybernetics, HongKong, 19-22 August 2007.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Dongyong Yang, Jinyin Chen and Matsumoto Naofumi, Self-adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization,ICNC 2007.
  6. J.H. Holland, Adaptation in Natural and Artificial System, The University of Michigan Press, Ann Arbor,1975.
  7. Goldberg D E, Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Corporation, Inc, 1989.
  8. Jianhua Liu and Xiaoping Fan, The Analysis and Improvement of Binary Particle Swarm Optimization, International Conference on Computational Intelligence and Security2009.
  9. Xu Jun and Huiyou Chang, The Discrete Binary Version Of The Improved Particle Swarm Optimization Algorithm, IEEE 2009.
  10. Javad Sadri and Ching Y. Suen, A Genetic Binary Particle Swarm Optimization Model, IEEE Congress on Evolutionary Computation2006.
  11. Zhang Li-ping, YU Huan-jun and HU Shang-xu, ”Optimal choice of parameters for particle swarm optimization”, Journal of Zhejiang University SCIENCE 2005, vol. 6A(6), pp. 528-534.
  12. Jaco F. Schutte and Albert A. Groenwold,”A Study of Global Optimization Using Particle Swarms”, Journal of Global Optimization (2005) vol. 31, pp. 93-108.
  13. J. Kennedy and R. Eberhart, A discrete binary version of the particle swarm optimization A. Proceeding of the conference on System, Man, and Cybernetics
  14. C, NJ, USA: IEEE Service Center, 1997, 4 104 - 4 109.
  15. M. Clerc, “Binary Particle Swarm Optimisers: Toolbox, Derivations, and Mathematical Insights,” 2005.
  16. Online. Available: http://clerc.maurice.free.fr/pso/.
  17. Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78–87. ISBN 3-540-58484-6.
Index Terms

Computer Science
Information Sciences

Keywords

Binary Particle Swarm Optimization BPSO Genetic Algorithm GA Hybrid Binary Particle Swarm Optimization HBPSO Crossover