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Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

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Abstract

In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm’s performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO’s efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO’s effectiveness and efficiency. The overall results highlight PKO’s exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko.

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Data availability

Data are available on request.

Notes

  1. https://en.wikipedia.org/wiki/Pied_kingfisher.

  2. https://pixabay.com/zh/photos/kingfisher-pied-kingfisher-3961031/

  3. https://pixabay.com/zh/photos/pied-kingfisher-hovering-bird-5957261/

Abbreviations

ABC:

Artificial bee colony

CS:

Cuckoo search

DE:

Differential algorithm

FA:

Firefly algorithm

GA:

Genetic algorithm

GSA:

Gravitational search algorithm

GWO:

Grey Wolf optimizer

HHO:

Harris Hawks optimization

LO:

Local optimum

MBO:

Monarch butterfly optimization

MFO:

Moth-flame optimization

NFL:

No free lunch

PKO:

Pied kingfisher optimizer

SA:

Simulated annealing

SCA:

Sine cosine algorithm

WSR:

Wilcoxon signed-rank

WOA:

Whale optimization algorithm

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Authors and Affiliations

  1. LSIB Laboratory, FST Mohammedia, Hassan II University of Casablanca, Mohammedia, Morocco

    Anas Bouaouda & Yassine Sayouti

  2. Faculty of Engineering, Helwan University, Cairo, Egypt

    Fatma A. Hashim

  3. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Fatma A. Hashim

  4. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Abdelazim G. Hussien

  5. Faculty of Science, Fayoum University, Faiyum, Egypt

    Abdelazim G. Hussien

  6. Applied Science Research Center, Applied Science Private University, 11931, Amman, Jordan

    Abdelazim G. Hussien

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  1. Anas Bouaouda
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  2. Fatma A. Hashim
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  4. Abdelazim G. Hussien
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Correspondence to Abdelazim G. Hussien.

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Bouaouda, A., Hashim, F.A., Sayouti, Y. et al. Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09879-5

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