## 1 Introduction
Based on the advantages of the golden eagle approach, which include fewer optimization parameters, robustness to local optima, and superior handling of high-dimensional problems, this algorithm model is suggested. This paper suggests a nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy to handle the problem. Arnold chaotic map initialization, nonlinear convex weight reduction, and a global optimization strategy make up the three components of the improved algorithm. An Arnold chaotic map with decent ergodicity is used to initialize the population of golden eagles in chaos maps. On the one hand, after examining several inertia weight types, nonlinear convex decreasing weights are utilized to help the golden eagle achieve improved convergence during the optimization process. On the other hand, in order to better achieve optimization, the golden eagle location update formula now includes the global optimization method, which connects each golden eagle to the generation's most optimized individual. Under 12 benchmark functions and CEC2021, IGEO is contrasted with other sophisticated optimization algorithms to demonstrate its better performance. The results of the numerical experiment demonstrate that IGEO has better optimization performance than other techniques. Using a single-strategy method, we assess the numerical optimization abilities of the original GEO algorithm, IGEO, and GEO for 12 benchmark functions. The outcomes of the numerical experiment show how well these two methods function as compared to the original GEO. Eventually, IGEO is transformed into a binary algorithm and used for cognitive radio spectrum allocation. Compared with other improved algorithms, IGEO achieves higher spectral efficiency. Five classic engineering cases were also used to further test the performance of the algorithm. The main contributions of this paper are as follows:  
1. The Arnold chaotic map strategy is employed to help the golden eagle population obtain a better initial position. 2. The nonlinear convex reduction strategy is used to coordinate the cruising and attacking behavior of the golden eagle, thereby improving its local and global search capabilities. 3. The global optimization strategy is utilized to assist the golden eagle population in complex environmental optimization, enhancing the possibility of escaping from local optima. 4. Twelve benchmark test functions and the CEC2021 test set are employed to assess the capabilities of the improved algorithm, including comparisons with eight other algorithms. * Various statistical tests, including the Wilcoxon rank-sum test and Friedman ranking, as well as [PERSON]'s subsequent verification, are conducted to determine the overall ranking of the comparison algorithms and the improved algorithm on the twelve benchmark test functions and CEC2021, thereby validating the final performance of the algorithm. * The individual effects of the nonlinear convex reduction strategy and the last global optimization strategy are tested separately. * Algorithm complexity analysis is performed. * The improved algorithm is applied to the classical problem of cognitive radio spectrum allocation and the results compared with those of commonly used algorithms. * Five common engineering applications are utilized to test the search capabilities of the algorithm. The following is a breakdown of this article. The second section goes over the fundamental ideas and the current state of GEO research. The third section offers a thorough explanation of the improved technique proposed in this article, and an analysis of its complexity. The fourth section, which mostly focuses on certain application components, contains simulation experiments and engineering best practices. In simulation experiments, there are compared experiments with a single improvement approach as well as comparative experiments between improved algorithms and alternative algorithms.