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. 2023;30(4):2683-2723.
doi: 10.1007/s11831-023-09883-3. Epub 2023 Jan 12.

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Farhad Soleimanian Gharehchopogh  1 , Alaettin Ucan  2 , Turgay Ibrikci  3 , Bahman Arasteh  4 , Gultekin Isik  5
Affiliations

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Farhad Soleimanian Gharehchopogh et al. Arch Comput Methods Eng. 2023.

Abstract

Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Conflict of interest statement

Conflict of interestThere is no conflict of interest statement.

Figures

Fig. 1
Fig. 1
Possible locations in 2-dimensional and 3-dimensional [15]
Fig. 2
Fig. 2
Assessment of fitness [15]
Fig. 3
Fig. 3
Trends of vb and vc [15]
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Fig. 4
Flowchart of SMA [15]
Fig. 5
Fig. 5
The steps of SMA [15]
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Fig. 6
Percentage of papers published with SMA in different publications
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Fig. 7
Total incidence of SMA Articles Published
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Fig. 8
Procedure for extracting papers belongs to the SMA algorithm
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Fig. 9
Classification of SMA methods
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Advantages of combining SMA with different algorithms
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Disadvantages of combining SMA with different algorithms
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Percentage of methods used for Improved SMA
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Fig. 13
Flowchart of the CSMA
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Fig. 14
The most critical chaotic goals in SMA
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Fig. 15
Flowchart of OBL and LF in SMA [89]
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The most critical positive goals by LF in SMA
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Fig. 17
Variants of SMA percentage chart based on two different methods
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Fig. 18
Number of papers in different areas of Optimization based on SMA
Fig. 19
Fig. 19
Percentage of SMA methods based on four different areas
See this image and copyright information in PMC

References

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