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A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

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  • Nicolás Caselli

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Ricardo Soto

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Broderick Crawford

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Sergio Valdivia

    (Dirección de Tecnologías de Información y Comunicación, Universidad de Valparaíso, Valparaíso 2361864, Chile)

  • Rodrigo Olivares

    (Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile)

Abstract

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

Suggested Citation

  • Nicolás Caselli & Ricardo Soto & Broderick Crawford & Sergio Valdivia & Rodrigo Olivares, 2021. "A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique," Mathematics, MDPI, vol. 9(16), pages 1-28, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1840-:d:608279
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    References listed on IDEAS

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    Cited by:

    1. Fabián Riquelme & Francisco Muñoz & Rodrigo Olivares, 2023. "A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1267-1285, September.

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