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. 2023 Apr 12:1-54.
doi: 10.1007/s11831-023-09923-y. Online ahead of print.

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rebika Rai  1 , Krishna Gopal Dhal  2
Affiliations

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rebika Rai et al. Arch Comput Methods Eng. .

Abstract

There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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Conflict of interest statement

Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest. The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Terminologies used for searching EO research papers from Google Scholar
Fig. 2
Fig. 2
Different EO related papers published in different journals and conferences (As per the survey)
Fig. 3
Fig. 3
Year-wise EO related research papers published (As per the survey)
Fig. 4
Fig. 4
Year-wise citations for the EO related research papers published (As per the survey)
Fig. 5
Fig. 5
Top 10 publishers publishing the EO-related research papers (As per the survey)
Fig. 6
Fig. 6
Top 10 journals publishing the EO-related research papers (As per the survey)
Fig. 7
Fig. 7
Flowchart of EO algorithm
Fig. 8
Fig. 8
Year-wise depiction of the various research article based on Basic EO (As per the survey)
Fig. 9
Fig. 9
Revised and Hybridized variants of EO
Fig. 10
Fig. 10
Year-wise depiction of the various research article based on Basic EO and variants of EO (As per the survey)
Fig. 11
Fig. 11
Different revised variants of EO
Fig. 12
Fig. 12
Year-wise depiction of the various research article based on revised variants of EO (As per the survey)
Fig. 13
Fig. 13
Proportions of research articles addressing the various types of revised EO. (As per the survey)
Fig. 14
Fig. 14
Hybridized variants of EO
Fig. 15
Fig. 15
Year-wise depiction of the various research article based on hybridized variants of EO (As per the survey)
Fig. 16
Fig. 16
Proportions of research articles addressing the various meta-heuristics algorithms hybridized with EO (As per the survey)
See this image and copyright information in PMC

References

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