The Coati Optimization Algorithm (COA) is a newly developed metaheuristic algorithm, drawing inspiration from the clever tactics Coatis use when attacking Iguanas as well as their strategies for dealing with and evading predators. This algorithm has shown a commendable level of effectiveness when compared to various other metaheuristic algorithms. Its performance metrics indicate that it outperforms many alternatives in terms of efficiency and results. To overcome challenges such as the imbalance between exploration and exploitation phases and become trapped in local optima for solving complex optimization problems, an innovative technique known as "Enhanced Opposition-Based Learning" (EOBL) has been integrated with the COA algorithm. This technique draws inspiration from Random Opposition-Based Learning methods and can effectively influence the balance between exploration and exploitation phases. The Enhanced of Coati Optimization Algorithm (EOBCOA) is a novel metaheuristic algorithm proposed to enhance the performance of the COA. This method has been applied on standard benchmark functions to improve the proposed optimization algorithm. To assess the effectiveness of the proposed EOBCOA method, it was tested on standard benchmark functions, including IEEE CEC2005, IEEE CEC2019, and seven engineering problems. The results show that the EOBCOA method outperforms other advanced algorithms in achieving global optimization.
Shadravan et al. (Wed,) studied this question.