An Improved Coati Optimization Algorithm Based on Specular Reflection Learning

In order to improve the convergence speed and solution accuracy of the Coati optimization algorithm (COA), an improved Coati Optimization algorithm (SRLCOA) based on specular reflection learning strategy is proposed. The initial population diversity is increased by introducing the specular reflection learning strategy in the population initialization, which improves the global search ability and convergence speed of the algorithm. Comparative experimental results show that compared with other intelligent algorithms, SRLCOA has certain advantages in the searching ability and convergence speed.