Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I (Lecture Notes in Computer Science)
Particle Swarm Optimization of Feed-Forward Neural Networks with Weight Decay
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A Hybrid Particle Swarm Optimization for Feed-Forward Neural Network Training
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Particle Swarm Trained Neural Network for Fault Diagnosis of Transformers by Acoustic Emission
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
International Journal of Bio-Inspired Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
Central force optimization on a GPU: a case study in high performance metaheuristics
The Journal of Supercomputing
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Central Force Optimization (CFO) is a novel and upcoming metaheuristic technique that is based upon physical kinematics. It has previously been demonstrated that CFO is effective when compared with other metaheuristic techniques when applied to multiple benchmark problems and some real world applications. This work applies the CFO algorithm to training neural networks for data classification. As a proof of concept, the CFO algorithm is first applied to train a basic neural network that represents the logical XOR function. This work is then extended to train two different neural networks in order to properly classify members of the Iris data set. These results are compared and contrasted to results gathered using Particle Swarm Optimization (PSO) in the same applications. Similarities and differences between CFO and PSO are also explored in the areas of algorithm design, computational complexity, and natural basis. The paper concludes that CFO is a novel and promising meta-heuristic that is competitive with if not superior to the PSO algorithm, and there is much room to further improve it.