Abstract
This paper employs a hybrid particle swarm optimization using optimal foraging theory (PSOOFT) for multilayer feed-forward neural network (MFNN) training. Three benchmark classification problems: Iris, Newthyroid and Glass are conducted to measure the performance of PSOOFT based MFNN. The simulation results are also compared with obtained using back Propagation (BP), genetic algorithm (GA) and standard PSO (SPSO) approaches to demonstrate the effectiveness and efficiency of PSOOFT.
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Niu, B., Li, L. (2008). A Hybrid Particle Swarm Optimization for Feed-Forward Neural Network Training. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_59
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DOI: https://doi.org/10.1007/978-3-540-85984-0_59
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