Gölcük, İ., Ozsoydan, F.B. and Durmaz, E.D. (2023) An Improved Arithmetic Optimization Algorithm for Training Feedforward Neural Networks under Dynamic Environments. Knowledge-Based Systems, 263, Article ID: 110274.
https://doi.org/10.1016/j.knosys.2023.110274

has been cited by the following article:

• TITLE: Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

  AUTHORS: Yu Liu, Minge Chen, Ran Yin, Jianwei Li, Yafei Zhao, Xiaohua Zhang

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

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.6, June 27, 2024

  ABSTRACT: 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.