Two Diverse Swarm Intelligence Techniques for Supervised Learning

Tad Gonsalves
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Tad Gonsalves: Department of Information and Communication Sciences, Sophia University, Tokyo, Japan

International Journal of Swarm Intelligence Research (IJSIR), 2015, vol. 6, issue 4, 55-66

Abstract: Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:6:y:2015:i:4:p:55-66

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