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Hybridization of Metaheuristic and Population-Based Algorithms with Neural Network Learning for Function Approximation

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Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

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Abstract

This paper attempts to improve the learning representation of radial basis function neural network (RBFNN) through metaheuristic algorithm (MHA) and evolutionary algorithm (EA). Next, the ant colony optimization (ACO)-based and genetic algorithm (GA)-based approaches are employed to train RBFNN. The proposed hybridization of ACO-based and GA-based approaches (HAG) algorithm incorporates the complementarity of exploration and exploitation abilities to reach resolution optimization. The property of population diversity has higher chance to search the global optimal instead of being restricted to local optimal extremely in two benchmark problems. The experimental results have shown that ACO-based and GA-based approaches can be integrated intelligently and develop into a hybrid algorithm which aims for receiving the best precise learning expression among relevant algorithms in this paper. Additionally, method assessment results for two benchmark continuous test function experiments and show that the proposed HAG algorithm outperforms relevant algorithms in term of preciseness for learning of function approximation.

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Authors and Affiliations

  1. Department of Business Administration, Hungkuo Delin University of Technology, No. 1, Ln 380, Qingyun Road, New Taipei City, 23654, Taiwan

    Zhen-Yao Chen

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  1. Zhen-Yao Chen
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Correspondence to Zhen-Yao Chen .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

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Chen, ZY. (2021). Hybridization of Metaheuristic and Population-Based Algorithms with Neural Network Learning for Function Approximation. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-88113-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

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