The Arithmetic Optimization Algorithm

• Published in: Computer methods in applied mechanics and engineering Vol. 376; p. 113609
• Main Authors: Abualigah, Laith, Diabat, Ali, Mirjalili, Seyedali, Abd Elaziz, Mohamed, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2021; Elsevier BV
• Subjects: Algorithm; Algorithms; AOA; Arithmetic; Arithmetic Optimization Algorithm; Artificial Intelligence; Benchmark; Computational Intelligence; Design engineering; Genetic Algorithm; Grey Wolf Optimizer; Heuristic; Heuristic methods; Meta-heuristics; Multiplication; Operators (mathematics); Optimization; Optimization algorithms; Particle Swarm Optimization; Subtraction; Whale Optimization Algorithm; Arithmetic Optimization Algorithm; Whale Optimization Algorithm; Artificial Intelligence; Genetic Algorithm; Algorithm; Optimization; Meta-heuristics; AOA; Computational Intelligence; Grey Wolf Optimizer; Benchmark; Particle Swarm Optimization; Heuristic
• ISSN: 0045-7825
• DOI: 10.1016/j.cma.2020.113609

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .