Mutation-based Arithmetic Optimization Algorithm for Global Optimization

Real-life optimization problems need an algorithm which efficiently explores the search area to obtain a global optimal solution. The arithmetic optimization algorithm (AOA) is a recently developed algorithm that performs search operation using the basic mathematic functions; Addition, multiplication, division, and Subtraction. However, the AOA stuck at the local optimum solution for some functions due to inadequate balance between diversification and intensification. Therefore, in the present work, a balance is provided between exploration and exploitation search mechanism by incorporating mutation strategy into the AOA and named as mutation-based arithmetic optimization algorithm (m-AOA). The proposed algorithm's performance is tested on twenty-three benchmark functions having different characteristics. Results obtained are compared in terms of performance parameters, including average, standard deviation, median, worst and best values with other optimization algorithms. The comparison suggested that the proposed method outperforms the other algorithms.