Parallel Swarms Oriented Particle Swarm Optimization
Joint Authors
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-05
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence.
In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima.
In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution.
In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best.
The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions.
The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance.
The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.
American Psychological Association (APA)
Gonsalves, Tad& Egashira, Akira. 2013. Parallel Swarms Oriented Particle Swarm Optimization. Applied Computational Intelligence and Soft Computing،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-496276
Modern Language Association (MLA)
Gonsalves, Tad& Egashira, Akira. Parallel Swarms Oriented Particle Swarm Optimization. Applied Computational Intelligence and Soft Computing No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-496276
American Medical Association (AMA)
Gonsalves, Tad& Egashira, Akira. Parallel Swarms Oriented Particle Swarm Optimization. Applied Computational Intelligence and Soft Computing. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-496276
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496276