Cauchy Mutation Chaotic Coati Optimization Algorithm

Coati Optimization Algorithm (COA) is a novel heuristic algorithm that simulates the intricate behavioral repertoire of coatis manifestations during their pursuit of iguanas as weil as their evasive tactics against predators. Aiming to elevate the convergence characteristics regarding speed and precision of the original algorithmic process, and addressing the issue of susceptibility to local optima, an enhanced Coati Optimization Algorithm is suggested based on Cauchy mutation and chaotic maps. Firstly, the Cauchy mutation is embedded in the process of coatis hunting for iguanas. Subsequently, each of ten chaotic maps was incorporated into the probing phase of the COA, generating ten unique enhanced versions. This augments the algorithm's precision in optimization, bolsters the equilibrium between exploration and exploitation and enhances its itinerancy and non-redundancy. The enhanced optimization algorithm exhibiting the aggregated optimal performance is selected from types of different enhanced COA and used for subsequent comparisons with other algorithms for function optimization and engineering optimization. Thirty benchmark functions from the CEC-BC-2022 datasets were incorporated for evaluation on performance metrics of the improved COA with Cauchy mutation and ten types of chaotic maps, and then the performance of the Chebyshev map enhanced COA and other swarm intelligent algorithms are compared for optimization purposes. Finally, optimization was performed on four distinct engineering design problems. The outcomes of the simulation experiment evidence that the advanced Cauchy mutation chaotic COA yields satisfactory outcomes in addressing both function optimization and engineering optimization. The algorithm exhibits superiority in balancing exploration and exploitation during the iterative procedure of optimization, thereby enhancing convergence precision.