An Improved Arithmetic Optimization Algorithm for Numerical Optimization Problems

Mengnan Chen, Yongquan Zhou and Qifang Luo
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Mengnan Chen: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Yongquan Zhou: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Qifang Luo: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China

Mathematics, 2022, vol. 10, issue 12, 1-27

Abstract: The arithmetic optimization algorithm is a recently proposed metaheuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) based on the population control strategy is introduced to solve numerical optimization problems. By classifying the population and adaptively controlling the number of individuals in the subpopulation, the information of each individual can be used effectively, which speeds up the algorithm to find the optimal value, avoids falling into local optimum, and improves the accuracy of the solution. The performance of the proposed IAOA algorithm is evaluated on six systems of nonlinear equations, ten integrations, and engineering problems. The results show that the proposed algorithm outperforms other algorithms in terms of convergence speed, convergence accuracy, stability, and robustness.

Keywords: arithmetic optimization algorithm; population control strategy; systems of nonlinear equations; numerical integrals; metaheuristic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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