A Comprehensive Survey on Seagull Optimization Algorithm and Its Variants

Abstract

Over the past few decades, researchers have developed several metaheuristic algorithms following smart rules and strategies for solving high dimension optimization problems. However, such rapid advancements in the development of metaheuristic algorithms and their diverse applications present challenges in evaluating their relative effectiveness and adaptability to various problem types. To this end, this paper presents a comprehensive review and systematic evaluation on seagull optimization algorithm (SOA), one of the recent metaheuristic swarm optimization algorithms, that mimics migrating and hunting behaviour of seagull sea birds, for inspiring researchers to perform further research in this field. The review initiates with critical screening and evaluation of SOA published articles based on strict criteria to select 109 eligible articles for comprehensive review. This paper examines and explores past research on SOA including advancement, modifications, multi-objective versions, hybridization and real-world application areas. Additionally, the SOA articles percentage distribution was visualized in terms of improvements, journals, various publishers, and different domains of optimization problems. Key findings indicate that SOA performance have been improved mostly utilizing chaotic map strategy in 22% and levy flight mechanism in 18% of SOA publications. It was also revealed that Springer, IEEE and Elsevier have higher number of SOA publications having 19, 16 and 15%, respectively. Finally, concluding remarks and future research directions are provided for further investigation on SOA, particularly in the field of biomedical applications and sensitive tuning of its internal parameters.