Evolutionary computation between Genetic Algorithm and Particle Swarm Optimization
Main Article Content
Abstract
- Genetic algorithm (GA) proved by many researchers that can solve optimization problems. However, GA lack on sharing information between the populations in consequent GA finds the solution quite slow. The new technique that is mentioned is particle swarm optimization (PSO). PSO is a technique in a group of evolutionary computation like GA. PSO find the best result by simulation moving of bird or fish to find foods or living, unlike GA. The GA uses the candidate of population to find the solution. This article presents techniques of PSO comparing with GA to point out the difference the process finding the solution.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
2. M. Srinivas, L. M. Patnaik, “Genetic algorithm: a survey”, IEEE computer society, Vol. 27, pp. 17-26, 1994
3. Carlos A. Coello, “An Updated Survey of GA-Based Multiobjective Optimization Techniques”, ACM Computing Survey (CSUR), Vol. 32, June 2000
4. Kennedy J. and Eberthart R. “Particle Swarm Optimization”, Proc. IEEE Int. Conf. Neural Network, Vol. 4, pp 1942-1948, 1995
5. Cezary Z. Janikow and Zbigniew Michalewicz, “An Experimental Comparison of Binary and Floating Point Representation in Genetic Algorithm”, Proceeding of the 4th International Conference on Genetic Algorithm, pp. 31-36, 1991
6. David E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning”, Addison Wesley, New York, 1989
7. Z. Michalewicz, “Genetic Algorithm + Data Structures = Evolution Programs”, Springer-Verlag, New York, 1992
8. P. E. Gill and W. Murray, “Quasi-Newton method for Unconstrained Optimization”, IMA Journal of Applied Mathematics, Vol. 9, pp. 91-108, 1972
9. Nelder J. A. and Mead R. , “A simplex method for function optimization”, The Computer Journal, Vol.7, pp. 308-313, 1965
10. A. Ratnawera, S. K. Halgamuge, and H. C. Watson, Selforganizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transaction on Evolutionary Computation, Vol.8, No.3, pp. 240-255, 2004
11. Angline P. J., “Evolutionary Optimization versus Particle Swarm Optimization: Philosophy and Performance Difference,” The 7th Annual Conference on Evolutionary Programming, San Diego, USA 1998
12. J. Vesterstrom, R. Thomsen, “A Comparative study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithm on Numerical Benchmark Problem,” in Proc. IEEE Congr Evolutionary Computation, Protland, pp. 1980-1987, 2004
13. Deniel W. Boeringer and Douglas H. Werner, “Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis”, IEEE Trans. On Antennas and Proparation, Vol. 52, No. 3, pp 771-779, March 2004
14. The Mathworks, “Genetic Algorithm and Direct Search Toolbox User’s Guide”, The Mathworks Inc, p.222, 2005