An Improved Central Force Optimization Algorithm for Multimodal Optimization
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-07
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization.
CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set.
When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing.
The comparison results demonstrate that the proposed algorithm is effective and efficient.
American Psychological Association (APA)
Liu, Jie& Wang, Yuping. 2014. An Improved Central Force Optimization Algorithm for Multimodal Optimization. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1039794
Modern Language Association (MLA)
Liu, Jie& Wang, Yuping. An Improved Central Force Optimization Algorithm for Multimodal Optimization. Journal of Applied Mathematics No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1039794
American Medical Association (AMA)
Liu, Jie& Wang, Yuping. An Improved Central Force Optimization Algorithm for Multimodal Optimization. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1039794
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1039794