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A New Hybrid Method Based on ACO and PSO with Fuzzy Dynamic Parameter Adaptation for Modular Neural Networks Optimization

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 940))

Abstract

Bio-inspired algorithms are metaheuristics that simulate insects or animal behavior for solving optimization problems. Ant colony Optimization (ACO) and Particle Swarm Optimization (PSO) are two of the main bio-inspired methods used to solve different kind of optimization problems. On the other hand, there are hybrid optimization methods consisting of the combination of two or more metaheuristics to solve a common set of problems. In this paper a new hy-brid method combining ACO and PSO is presented. This method is tested with the optimization of modular neural networks (MNNs) architectures. The MNN learns to classify images of human faces from the ORL face database. Another interesting contribution of this work is the implementation of fuzzy inference sys-tems to dynamically adjust the values of some parameters for the metaheuristics used in this paper. Therefore, in this paper, the main idea is to design a hybrid method to optimize the modular neural networks architectures. The achieved re-sults using the proposed hybrid method outperform the results with the individual optimization methods. The main contribution is the proposed new hybrid meta-heuristic that can solve complex problems more efficiently than ACO or PSO individually.

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Acknowledgements

We would like to express our gratitude to CONACYT, and Tecnológico Nacional de México/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Fevrier Valdez, Juan Carlos Vazquez & Patricia Melin

Authors
  1. Fevrier Valdez
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  2. Juan Carlos Vazquez
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  3. Patricia Melin
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Corresponding author

Correspondence to Fevrier Valdez .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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Valdez, F., Vazquez, J.C., Melin, P. (2021). A New Hybrid Method Based on ACO and PSO with Fuzzy Dynamic Parameter Adaptation for Modular Neural Networks Optimization. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_20

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