An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recen...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 56; no. 11; pp. 13187 - 13257
Main Authors: Rajwar, Kanchan, Deep, Kusum, Das, Swagatam
Format: Journal Article
Language:English
Published: England Springer 01.11.2023
Springer Nature B.V
Springer Netherlands
Subjects:
Algorithms
Application
Classification
Community research
Heuristic methods
Industrialization
Mathematical optimization
Newcomers
Optimization
Optimization techniques
Property
Taxonomy
Parameter
Metaheuristic algorithm
Optimization
Nature inspired algorithm
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10470-y