A comprehensive review on meta-heuristic algorithms and their classification with novel approach

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various field...

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Veröffentlicht in:Journal of Applied Research on Industrial Engineering Jg. 8; H. 1; S. 63 - 89
Hauptverfasser: Hojatollah Rajabi Moshtaghi, Abbas Toloie Eshlaghy, Mohammad Reza Motadel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Ayandegan Institute of Higher Education, Iran 01.03.2021
Schlagworte:
classification of meta-heuristic algorithms
evolutionary algorithms
meta-heuristic algorithms
meta-heuristic optimization
swarm algorithms
ISSN:2538-5100
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Zusammenfassung:Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.
ISSN:2538-5100
DOI:10.22105/JARIE.2021.238926.1180