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Metaheuristics: a comprehensive overview and classification along with bibliometric analysis

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Abstract

Research in metaheuristics for global optimization problems are currently experiencing an overload of wide range of available metaheuristic-based solution approaches. Since the commencement of the first set of classical metaheuristic algorithms namely genetic, particle swarm optimization, ant colony optimization, simulated annealing and tabu search in the early 70s to late 90s, several new advancements have been recorded with an exponential growth in the novel proposals of new generation metaheuristic algorithms. Because these algorithms are neither entirely judged based on their performance values nor according to the useful insight they may provide, but rather the attention is given to the novelty of the processes they purportedly models, these area of study will continue to periodically see the arrival of several new similar techniques in the future. However, there is an obvious reason to keep track of the progressions of these algorithms by collating their general algorithmic profiles in terms of design inspirational source, classification based on swarm or evolutionary search concept, existing variation from the original design, and application areas. In this paper, we present a relatively new taxonomic classification list of both classical and new generation sets of metaheuristic algorithms available in the literature, with the aim of providing an easily accessible collection of popular optimization tools for the global optimization research community who are at the forefront in utilizing these tools for solving complex and difficult real-world problems. Furthermore, we also examined the bibliometric analysis of this field of metaheuristic for the last 30 years.

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Source from left—commensalism (Oxpeckers on a Rhinoceros back), mutualism (Cattle egrets and Cattle), and parasitism (Mosquito feeding on Human blood) (Ezugwu and Prayogo 2019)

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Author notes

  1. Amit K. Shukla

    Present address: Faculty of Information Technology, University of Jyväskylä, Box 35 (Agora), Jyväskylä, 40014, Finland

Authors and Affiliations

  1. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, King Edward Road, Pietermaritzburg, 3201, South Africa

    Absalom E. Ezugwu & Jeffery O. Agushaka

  2. Department of Computer Science, South Asian University, New Delhi, 110021, India

    Amit K. Shukla, Rahul Nath & Pranab K. Muhuri

  3. Department of Computer Science and Informatics, University of the Free State, Bloemfontein, 9301, South Africa

    Andronicus A. Akinyelu

  4. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yulin, Taiwan

    Haruna Chiroma

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Contributions

Conception or design of the work, AEE; Data curation, AEE, AKS, AAA, and JOA; Formal analysis, AEE, AKS, and RN; Methodology, AEE, AKS, RN and HC; Supervision, AEE; Validation, AEE, AKS and PKM; Visualization, AKS; Drafting of original manuscript, AEE, AKS, and RN; Drafting—review and editing, AEE, AKS, RN, HC, and PKM.

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Ezugwu, A.E., Shukla, A.K., Nath, R. et al. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 54, 4237–4316 (2021). https://doi.org/10.1007/s10462-020-09952-0

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