An Improved Arithmetic Optimization Algorithm

This paper focuses on developing an improved arithmetic optimization algorithm to achieve better convergence during exploration and exploitation phases. The proposed algorithm has been achieved by incorporating other functions like square, cube, sine, and cosine in the algorithms’ stochastic scaling coefficient. Further, a comparison between the proposed method and the conventional technique is made on various benchmark functions. It is observed from the numerical results is that the proposed algorithm with both sin and cos have performed better concerning mean, best, and standard deviation values. Moreover, on achieving the worst global minima, the proposed algorithm showed better performance for most minor functions. The results also highlight that the proposed algorithm has shown the best performance, especially for higher-dimensional systems.