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Deterministic Parameter Selection of Artificial Bee Colony Based on Diagonalization

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Hybrid Intelligent Systems (HIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 923))

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Abstract

Artificial Bee Colony (ABC) is a bee inspired swarm intelligence (SI) algorithm well-known for its versatility and simplicity. In crucial steps of the algorithm, employed and scout bees phase, parameters (decision variables) are chosen in a random fashion. Although this randomness may apparently not influence the overall performance of the algorithm, it may contribute to premature convergence towards bad local optima or lack of exploration in multimodal problems featuring rugged surfaces. In this study, a deterministic selection method for decision variables based on Cantor’s proof of uncountability of rational numbers is proposed to be used in the aforementioned steps. The approach seeks to eliminate stochasticity, enhance the exploratory capabilities of the algorithm by verifying all possible variables, and provide a better mechanism to displace solutions out of local optima, introducing more novelty to solutions. In order to analyze potential benefits brought by the proposed approach to the overall performance of the ABC, three variants featuring modifications discussed in this work were designed to be compared in terms of efficiency and stability against the original ABC on 15 instances of unconstrained optimization problems.

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Author information

Authors and Affiliations

  1. Systems Optimization Laboratory, University of Tsukuba, Tsukuba, Japan

    Marco Antonio Florenzano Mollinetti & Takahito Kuno

  2. Science Faculty, University of Porto, Porto, Portugal

    Mario Tasso Ribeiro Serra Neto

Authors
  1. Marco Antonio Florenzano Mollinetti
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  2. Mario Tasso Ribeiro Serra Neto
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  3. Takahito Kuno
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Corresponding author

Correspondence to Marco Antonio Florenzano Mollinetti .

Editor information

Editors and Affiliations

  1. School of Engineering, Instituto Superior de Engenharia (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Department of Production and Systems, University of Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Florenzano Mollinetti, M.A., Tasso Ribeiro Serra Neto, M., Kuno, T. (2020). Deterministic Parameter Selection of Artificial Bee Colony Based on Diagonalization. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_9

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