Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms

Abstract

Meta heuristics is an optimization approach that works as an intelligent technique to solve optimization problems. Evolutionary algorithms, human-based algorithms, physics-based algorithms and swarm intelligence are categorized under meta-heuristic algorithms. This study presents a critical review of meta-heuristic algorithms for future reference, including concepts, applications, advantages and disadvantages, before focusing on one specific meta-heuristic algorithm, namely, Emperor Penguin Optimizer (EPO). It is an intelligent algorithm developed after observing the behaviour of emperor penguins during cold winters. This technique was introduced by Dhiman in 2018 and adopted to solve optimization problems. The study reviews the algorithm variants starting from its invention in 2018 until 2022. The literature is comprehensively reviewed to reflect on the progress of the algorithm’s adoption, highlighting a new area for improvement. The most significant result is that the proposed algorithm has been proven an effective technique. The merits and demerits of the algorithm are explored to provide valuable perspectives for future research. This study answers the question regarding meta-heuristic algorithms’ effectiveness, especially EPO. Both beginners and experts of EPO research can use the findings of this study as guidelines for enhancing current concepts and applications of state-of-the-art algorithms for future development works.

Keywords

• Meta heuristic
• Emperor Penguin Optimizer
• EPO variants
• Optimization algorithms
• Huddling behaviour
• Swarm intelligence