Improved arithmetic optimization algorithm

The Arithmetic Optimization Algorithm (AOA) is a well-established metaheuristic optimization technique inspired by fundamental mathematical operations such as addition, subtraction, multiplication, and division. Although AOA has demonstrated strong performance in solving a variety of optimization problems, it encounters significant challenges in complex search spaces. These challenges include limited exploration capability, premature convergence to local optima, an imbalance between exploration and exploitation, and slow convergence speed. To address these challenges, this study introduces the Improved Arithmetic Optimization Algorithm (IAOA), which incorporates Random Opposition-Based Learning (ROBL) to enhance the performance of AOA. ROBL improves AOA by introducing opposite solutions, thereby diversifying the search process to enhance exploration. It also refines exploitation by ensuring a more focused search within promising regions, achieving a better balance between exploration and exploitation. These enhancements allow IAOA to navigate complex search spaces effectively, avoid local optima, and accelerate convergence toward the global solution. The performance of IAOA is evaluated on a standard set of 23 benchmark functions, including unimodal functions to test exploitation capabilities and multimodal functions to evaluate exploration. Results demonstrate that IAOA outperforms the original AOA on th e majority of benchmark functions, showcasing significant improvements in convergence speed, robustness, and overall optimization performance.