Open Access
Translator Disclaimer
2014 An Improved Central Force Optimization Algorithm for Multimodal Optimization
Jie Liu, Yu-ping Wang
J. Appl. Math. 2014: 1-12 (2014). DOI: 10.1155/2014/895629

Abstract

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.

Citation

Download Citation

Jie Liu. Yu-ping Wang. "An Improved Central Force Optimization Algorithm for Multimodal Optimization." J. Appl. Math. 2014 1 - 12, 2014. https://doi.org/10.1155/2014/895629

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131952
MathSciNet: MR3293001
Digital Object Identifier: 10.1155/2014/895629

Rights: Copyright © 2014 Hindawi

JOURNAL ARTICLE
12 PAGES


SHARE
Vol.2014 • 2014
Back to Top