Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

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.

Automated summary

This article introduces many algorithms inspired by various events observable in nature, covering evolutionary phenomena, social behavior, physical processes, chemical reactions, human behavior, intelligence, plant behavior, numerical techniques, and mathematics programming. Equilibrium Optimizer (EO), a nature-inspired metaheuristic algorithm based on physics, is thoroughly reviewed along with its variations. Strengths and weaknesses of the algorithm are discussed, and core optimization problems solved by EO in various applications are covered, including image classification and scheduling. The article concludes with recommendations for future EO research.