Author(s)

Yu Liu¹, Minge Chen^1*, Ran Yin¹, Jianwei Li¹, Yafei Zhao¹, Xiaohua Zhang^2#

¹School of Mathematics and Data Science, Shaanxi University of Science & Technology, Xi’an, China.
²School of Materials Science and Engineering, Shaanxi University of Science & Technology, Xi’an, China.

This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.

Arithmetic Optimization Algorithm, Adaptive Balance Factor, Spiral Search, Brownian Motion, Whale Fall Mechanism

Liu, Y. , Chen, M. , Yin, R. , Li, J. , Zhao, Y. and Zhang, X. (2024) Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design. Journal of Applied Mathematics and Physics, 12, 2212-2253. doi: 10.4236/jamp.2024.126134.