The Arithmetic Optimization Algorithm

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 imp...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 376; s. 113609
Hlavní autoři: Abualigah, Laith, Diabat, Ali, Mirjalili, Seyedali, Abd Elaziz, Mohamed, Gandomi, Amir H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.04.2021
Elsevier BV
Témata:
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
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Popis
Shrnutí: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 .
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ISSN:0045-7825
DOI:10.1016/j.cma.2020.113609