Slime mould algorithm: a comprehensive review of recent variants and applications

Slime Mould Algorithm (SMA) has recently received much attention from researchers because of its simple structure, excellent optimisation capabilities, and acceptable convergence in dealing with various types of complex real-world problems. this study aims to retrieve, identify, summarise and analys...

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Vydáno v:International journal of systems science Ročník 54; číslo 1; s. 204 - 235
Hlavní autoři: Chen, Huiling, Li, Chenyang, Mafarja, Majdi, Heidari, Ali Asghar, Chen, Yi, Cai, Zhennao
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 02.01.2023
Taylor & Francis Ltd
Témata:
Algorithms
computational intelligence
Domains
Heuristic methods
meta heuristic algorithm
Optimization
Slime
Slime mould algorithm
swarm intelligence
ISSN:0020-7721, 1464-5319
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Shrnutí:Slime Mould Algorithm (SMA) has recently received much attention from researchers because of its simple structure, excellent optimisation capabilities, and acceptable convergence in dealing with various types of complex real-world problems. this study aims to retrieve, identify, summarise and analyse critical studies related to SMA development. Based on this, 98 SMA-related studies in the Web of Science were retrieved, selected, and identified. The two main review vectors were advanced versions of SMAs and application domains. First, we counted and analysed various advanced versions of SMAs, summarised, classified, and discussed their improvement methods and directions. Secondly, we sort out the application domains of SMA and analyse the role, development status, and shortcomings of SMA in each domain. A survey based on the existing literature shows that SMAs clearly outperform some established metaheuristics in terms of speed and accuracy in handling various benchmark problems and solving multiple realistic optimization problems. This review not only suggests possible future directions in the field but, due to the inclusion of graphical and tabular comparisons of various properties, also provides future researchers with a comprehensive source of information about SMA and advanced versions of SAMs and the scope of adaptation for multiple application domains.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2022.2153635