Augmented arithmetic optimization algorithm using opposite-based learning and levy flight distribution for global optimization and data clustering

This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Levy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.

Automated summary

This paper proposes a new data clustering method by using the advantages of metaheuristic optimization algorithms. A novel arithmetic optimization algorithm (AOA) is introduced to address complex optimization tasks. By integrating opposition-based learning (OLB) and Levy flight (LF) distribution, a new variant of AOA called Augmented AOA (AAOA) is developed to improve the exploration and exploitation trends of the traditional AOA. Extensive experiments and comparisons with other optimization algorithms demonstrate the superiority of AAOA in both benchmark functions and data clustering datasets.