A comprehensive review on expansion and statistical evaluation of Coati optimization algorithm

The Coati optimization algorithm (COA) is a recently introduced metaheuristic algorithm inspired from the hunting and escaping activities of Coati in their habitat. Since its development, COA has achieved new heights owing its efficacy in solving diverse optimization problems, flexibility and simple procedure. Alike other metaheuristic optimization algorithms, COA based on intelligent metaphor have seen rapid advancements and improvements, however with similar intrinsic weaknesses. The probable reason being inadequate investigations on COA procedure, limitations, statistical evaluation prior to its enhancement. The importance of the current review stems from its in-depth exploration of COA advancements, foundation principles and practical scenarios. This comprehensive review features 91 COA articles for analysing its adaptability and increasing interest in solving non-trivial optimization problems. It precisely explores the COA expansion, researching historical developments and modifications that enhances the algorithm capability in adapting to diverse landscapes of optimization problems. This review investigates the COA articles in terms of year wise publications, including enhancements, chaotic, opposition based learning variants, hybridizations, binary version, multi-objective variants, and different applications. It was shown that Elsevier and Springer have published highest number of COA papers having 54 and 51 counts, respectively. The published articles on classical COA confirm that it is mostly utilized for solving complex optimization problems in the field of electrical engineering (35%), I-o-T (22%) and medical diagnostics (17%) applications. Moreover, it is established that the COA performance has been improved by mostly hybridizing with other metaheuristic algorithms (29%), followed by introducing chaotic mapping (24%) and OBL scheme (15%), respectively. In addition, the present review significantly assesses the COA’s convergence behaviour and statistical performance in comparison to some well-known recent metaheuristic algorithms. Finally, concluding remarks with future research directions were recommended for scholars and scientists including improvements and critical suggestion for minimizing its limitations.