A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

Abstract

A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm’s ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy.

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

“…Deng et al [ 23 ] made significant improvements to the traditional GEO algorithm. In the process of population initialization, they introduced the Arnold Chaotic Map to generate a more diversified initial position, thereby enhancing the global search capability of the algorithm.…”

“…Equation (22) describes the process of parameter initialization; for each golden eagle i and each dimension j, the initial position X ij can be updated by introducing the chaos operator map ij Equation ( 22) after the re-updating of the equation. The new position x t+1 is updated using Equation (23).…”