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Link to original content: https://doi.org/10.1007/s11063-007-9053-x
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An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks

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Abstract

This paper presents a new evolutionary artificial neural network (ANN) algorithm named IPSONet that is based on an improved particle swarm optimization (PSO). The improved PSO employs parameter automation strategy, velocity resetting, and crossover and mutations to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. IPSONet uses the improved PSO to address the design problem of feedforward ANN. Unlike most previous studies on only using PSO to evolve weights of ANNs, this study puts its emphasis on using the improved PSO to evolve simultaneously structure and weights of ANNs by a specific individual representation and evolutionary scheme. The performance of IPSONet has been evaluated on several benchmarks. The results demonstrate that IPSONet can produce compact ANNs with good generalization ability.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200240, P.R. China

    Jianbo Yu, Lifeng Xi & Shijin Wang

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  1. Jianbo Yu
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  2. Lifeng Xi
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  3. Shijin Wang
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Correspondence to Jianbo Yu.

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Yu, J., Xi, L. & Wang, S. An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks. Neural Process Lett 26, 217–231 (2007). https://doi.org/10.1007/s11063-007-9053-x

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  • DOI: https://doi.org/10.1007/s11063-007-9053-x

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