A Hybrid Central Force Optimization Algorithm for Optimizing Ontology Alignment

2017 ◽  
pp. 334-343
Author(s):  
Xingsi Xue ◽  
Shijian Liu ◽  
Jinshui Wang
Keyword(s):  
Optimization Algorithm ◽  
Central Force ◽  
Ontology Alignment ◽  
Force Optimization ◽  
Central Force Optimization

Convergence analysis and performance of an extended central force optimization algorithm

2012 ◽  
Vol 219 (4) ◽  
pp. 2246-2259 ◽  
Author(s):  
Dongsheng Ding ◽  
Donglian Qi ◽  
Xiaoping Luo ◽  
Jinfei Chen ◽  
Xuejie Wang ◽  
...  
Keyword(s):  
Convergence Analysis ◽  
Optimization Algorithm ◽  
Central Force ◽  
Force Optimization ◽  
And Performance ◽  
Central Force Optimization

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

2019 ◽  
Vol 78 (18) ◽  
pp. 26373-26397 ◽  
Author(s):  
Heba M. El-Hoseny ◽  
Zeinab Z. El Kareh ◽  
Wael A. Mohamed ◽  
Ghada M. El Banby ◽  
Korany R. Mahmoud ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Central Force ◽  
Medical Image Fusion ◽  
Force Optimization ◽  
Histogram Matching ◽  
Fusion Approach ◽  
Central Force Optimization

scholarly journals An Improved Central Force Optimization Algorithm for Multimodal Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Liu ◽  
Yu-ping Wang
Keyword(s):  
Central Force ◽  
Multimodal Optimization ◽  
High Dimensions ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Clustering Technique ◽  
Force Optimization ◽  
Comparison Results ◽  
Complex Statistical Analysis ◽  
Central Force Optimization

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.

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