The Arithmetic Optimization Algorithm (AOA) is a novel meta-heuristic optimization algorithm that utilizes the mathematical properties of basic arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems. The algorithm is designed to explore and exploit search spaces efficiently, balancing global exploration with local intensification. AOA is implemented as a population-based algorithm that does not require derivative calculations, making it suitable for a wide range of optimization tasks. The algorithm's performance is evaluated on twenty-nine benchmark functions and several real-world engineering design problems, demonstrating its effectiveness compared to other well-known optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The AOA algorithm is structured into two main phases: exploration and exploitation. During the exploration phase, the algorithm uses division and multiplication operators to search for diverse solutions across the search space. In the exploitation phase, subtraction and addition operators are used to refine solutions and improve their quality. The algorithm's performance is influenced by parameters such as the control parameter μ and the sensitivity parameter α, which are adjusted to balance exploration and exploitation. The AOA algorithm is tested on various benchmark functions and real-world engineering problems, including welded beam design, tension/compression spring design, pressure vessel design, 3-bar truss design, and speed reducer design. The results show that AOA outperforms other optimization algorithms in terms of solution quality and convergence speed. The algorithm's computational complexity is analyzed, and it is found to be efficient for both low-dimensional and high-dimensional problems. The AOA algorithm is also compared with other optimization algorithms using statistical tests such as the Friedman ranking test and Wilcoxon signed-rank test, confirming its superiority in most cases. The algorithm's ability to avoid local optima and find high-quality solutions makes it a promising tool for solving complex optimization problems. Future research directions include exploring the integration of AOA with other stochastic components and applying it to various disciplines such as neural networks, image processing, and big data applications.The Arithmetic Optimization Algorithm (AOA) is a novel meta-heuristic optimization algorithm that utilizes the mathematical properties of basic arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems. The algorithm is designed to explore and exploit search spaces efficiently, balancing global exploration with local intensification. AOA is implemented as a population-based algorithm that does not require derivative calculations, making it suitable for a wide range of optimization tasks. The algorithm's performance is evaluated on twenty-nine benchmark functions and several real-world engineering design problems, demonstrating its effectiveness compared to other well-known optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The AOA algorithm is structured into two main phases: exploration and exploitation. During the exploration phase, the algorithm uses division and multiplication operators to search for diverse solutions across the search space. In the exploitation phase, subtraction and addition operators are used to refine solutions and improve their quality. The algorithm's performance is influenced by parameters such as the control parameter μ and the sensitivity parameter α, which are adjusted to balance exploration and exploitation. The AOA algorithm is tested on various benchmark functions and real-world engineering problems, including welded beam design, tension/compression spring design, pressure vessel design, 3-bar truss design, and speed reducer design. The results show that AOA outperforms other optimization algorithms in terms of solution quality and convergence speed. The algorithm's computational complexity is analyzed, and it is found to be efficient for both low-dimensional and high-dimensional problems. The AOA algorithm is also compared with other optimization algorithms using statistical tests such as the Friedman ranking test and Wilcoxon signed-rank test, confirming its superiority in most cases. The algorithm's ability to avoid local optima and find high-quality solutions makes it a promising tool for solving complex optimization problems. Future research directions include exploring the integration of AOA with other stochastic components and applying it to various disciplines such as neural networks, image processing, and big data applications.