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Review on Nature-Inspired Algorithms

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  • Wael Korani

    (University of Regina)

  • Malek Mouhoub

    (University of Regina)

Abstract

Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible. This is achieved by optimizing a set of objectives while meeting the system requirements. Optimization techniques are classified into exact and approximate algorithms. Nature-inspired (NI) methods, a sub-class of approximate techniques, are widely recognized for providing efficient approaches for solving a wide variety of real-world optimization problems. In this paper, we discuss many scenarios where we can or cannot use different NI methods in tackling real-world optimization problems. We also enrich our survey with many studies for the reader to prove the efficiency and efficacy of using NI methods to tackle many real-world applications. Therefore, NI methods should be considered as alternative reliable approaches in the absence of exact methods to provide satisfactory solutions.

Suggested Citation

  • Wael Korani & Malek Mouhoub, 2021. "Review on Nature-Inspired Algorithms," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, September.
  • Handle: RePEc:spr:snopef:v:2:y:2021:i:3:d:10.1007_s43069-021-00068-x
    DOI: 10.1007/s43069-021-00068-x
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    2. Chen, Ping-Yang & Chen, Ray-Bing & Chen, Yu-Shi & Wong, Weng Kee, 2023. "Numerical Methods for Finding A-optimal Designs Analytically," Econometrics and Statistics, Elsevier, vol. 28(C), pages 155-162.
    3. Adil Korchi & Fayçal Messaoudi & Ahmed Abatal & Youness Manzali, 2023. "Machine Learning and Deep Learning-Based Students’ Grade Prediction," SN Operations Research Forum, Springer, vol. 4(4), pages 1-21, December.

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