Particle Swarm Optimization with Flexible Swarm for Unconstrained Optimization
Keywords:
Particle Swarm Optimization Algorithm, Particle Swarm Optimization Algorithm with Flexible Swarm, Unconstrained OptimizationAbstract
Particle Swarm Optimization (PSO) algorithm inspired from behavior of bird flocking and fish schooling. It is well-known algorithm which has been used in many areas successfully. However it sometimes suffers from premature convergence. In resent year’s researches have been introduced a various approaches to avoid of this problem. This paper presents the particle swarm optimization algorithm with flexible swarm (PSO-FS). The new algorithm was evaluated on 14 functions often used to benchmark the performance of optimization algorithms. PSO-FS algorithm was compared to some other modifications of PSO. The results show that PSO-FS always performed one of the better results.
Downloads
References
Abd-El-Waheda WF., Mousab AA., El-Shorbagy MA (2011). Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. Journal of Computational and Applied Mathematics 235:1446–1453.
Akbari R. Ziarati K (2011). A rank based particle swarm optimization algorithm with dynamic adaptation, Journal of Computational and Applied Mathematics, 235(8):2694–2714.
Ali MM., Kaelo P (2008). Improved particle swarm algorithms for global optimization. Applied Mathematics and Computation 196:578–593.
Alrashidi MR., El-Hawary ME (2006). A Survey of Particle Swarm Optimization Applications in Power System Operations, Electric Power Components and Systems, 34/12:1349 — 1357.
Baskar S., Suganthan PN (2004). A Novel Concurrent Particle Swarm Optimization. Proceedings of the Congress on Evolutionary Computation, 792-796.
Blackwell T., Bratton D (2008). Examination of Particle Tails, Journal of Artificial Evolution and Applications, 8:1-10.
Bratton D., Kennedy J (2007). Defining a Standard for Particle Swarm Optimization, Proceedings of the 2007 IEEE Swarm Intelligence Symposium.
Bratton D. and Blackwell T (2008). A Simplified Recombinant PSO. Journal of Artificial Evolution and Applications, 8:1-10.
Chen CC (2011). Two-layer particle swarm optimization for unconstrained optimization problems. Applied Soft Computing, 11(1): 295-304
Chen TY., Chi TM (2010). On the improvements of the particle swarm optimization algorithm. Advances in Engineering Software 41:229–239.
He S., Wu QH, Wen JY, Saunders JR, Paton RC (2004). A particle swarm optimizer with passive congregation. BioSystems 78:135–147.
Jiang Y., Hu T., Huang CC, Wu X (2007). An improved particle swarm optimization algorithm. Applied Mathematics and Computation 193:231–239.
Kang Q., Wang L., Wu Q (2008). A novel ecological particle swarm optimization algorithm and its population dynamics analysis. Applied Mathematics and Computation 205:61–72.
Kennedy J., Eberhart R (1995). Particle Swarm Optimization, IEEE International Conference on Neural Networks.
Kok S., Snyman JA (2008). A Strongly Interacting Dynamic Particle Swarm Optimization Method. Journal of Artificial Evolution and Applications. 28:1-9.
Marinakis Y., Marinaki M., Dounias G (2008). Particle swarm optimization for pap-smear diagnosis, Expert Systems with Applications, 35:1645–1656.
Pena J., Upegui A., Sanchez E (2006). Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware, Proceedings of the First NASA/ESA Conference on Adaptive Hardware and Systems.
Van den Bergh F., Engelbrecht AP (2004). A cooperative approach to particle swarm optimization, IEEE Trans Evolut Comput, 8(3) 225–39.
Wang Z., Sun X., Zhang. D (2007). A PSO-Based Classification Rule Mining Algorithm, ICIC 2007, LNAI 4682: 377–384.
Zhao Y., Zu W., Zeng H (2009). A modified particle swarm optimization via particle visual modeling analysis, Computers and Mathematics with Applications, 57(11-12):2022-2029.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
