Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Nature-inspired metaheuristic algorithms are widely used computing paradigms.
• The Equilibrium Optimizer (EO) is a physics-based optimization algorithm inspired by dynamic source and sink models.

Purpose of the Study:

• To provide a comprehensive review of the Equilibrium Optimizer (EO) and its variations.
• To analyze the strengths and weaknesses of different EO variants.
• To identify core optimization problems and application areas for EO.

Main Methods:

• Systematic literature review of 175 research articles from major publishers.
• Analysis of EO's theoretical foundations and its adaptation in various modifications.
• Examination of EO's performance across diverse application domains.

Main Results:

• Identified numerous variations and modifications of the Equilibrium Optimizer.
• Discussed the advantages and limitations of EO and its variants for different problem types.
• Cataloged EO's application in areas such as image classification and scheduling problems.

Conclusions:

• The Equilibrium Optimizer is a recognized and adaptable nature-inspired algorithm.
• Understanding EO's strengths and weaknesses aids researchers in selecting appropriate variants.
• Future research directions for EO are identified to further enhance its capabilities.