Improved coati optimization algorithm through multi-strategy integration: from theoretical design to engineering applications

Optimization problems are crucial for a wide range of engineering applications, as efficient solutions lead to better performance. This study introduces an improved coati optimization algorithm (ICOA) that overcomes the primary limitations of the original coati optimization algorithm (COA), notably its insufficient population diversity and propensity to become trapped in local optima. To address these issues, the ICOA integrates three innovative strategies: Latin hypercube sampling (LHS), Lévyflight, and an adaptive local search. LHS is employed to ensure a diverse initial population, thereby laying a foundation for the optimization. Lévy-flight is utilized to facilitate an efficient global search, enhancing the algorithm’s ability to explore the solution space. The adaptive local search is designed to refine solutions, enabling more precise local exploration. Together, these strategies significantly improve the population’s quality and diversity, thereby improving the algorithm’s convergence accuracy and optimization capabilities. The performance of the ICOA is tested against several established algorithms, using 12 benchmark functions. Additionally, the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem, specifically the design optimization of tension/compression springs. Simulation results show that the ICOA consistently outperforms the other algorithms, providing robust solutions for a wide range of optimization problems.