## 1 Introduction
Due to the dispersed nature of swarm intelligence algorithms, they must iterate repeatedly in order to discover the optimum solution, which slows down the algorithm. Simultaneously, the algorithm might enter a local optimum and continually look for an optimum in a small area that is not the best option, causing it to become stuck. Researchers have recently presented some enhanced swarm intelligence algorithms and used them in various industries. [PERSON] et al. [14] proposed improved variants of Particle Swarm Optimization that incorporate novel modifications and demonstrate superior performance compared to conventional approaches in solving optimization problems and training artificial neural networks. A Fusion Multi-Strategy Marine Predator Algorithm for mobile robot path planning (FMMPA) was proposed by [PERSON] [15]. A better marine predator algorithm (MPA-DMA) was proposed by [PERSON] et al. [16], and it was used for feature selection with positive outcomes. However, the neighborhood clustering learning technique makes it simple to push the population beyond the cap, which is likely to have some bearing on how practical issues are resolved. A marine predator method based on mirror reflection urban and rural learning (PRIL-MPA), which was also applied in feature selection, was proposed by [PERSON] et al. [17]. In contrast to the previous method, in this article, an ideal person is identified to serve as a search guide for others, population variety is reduced, the search space is reduced, and local optimization is made simple. A marine predator algorithm (MSIMPA) combining chaotic opposition and collective learning, and which also offered the best individual guidance approach, was proposed by [PERSON] et al. [18]. The issue remains the same, and group learning has the potential to result in extended running times, as well. The use of cognitive radio has benefited from the work of numerous academics. The whale algorithm (IWOA) was enhanced by [PERSON] [19] and others to allocate the cognitive radio spectrum. The enhanced whale method outperforms competing algorithms thanks to a nonlinear convergence factor and a hybrid reverse learning strategy. Similar to this, the same authors enhanced the Grey Wolf Algorithm (IBGWO) [20] for usage in this field and employed three strategies: the nonlinear convergence factor, the Cauchy perturbation approach, and adaptive weight, achieving improved outcomes. [PERSON] et al. [21] adopted a novel strategy in which the industrial internet of things was recognized as an environment, and applied the improved sparrow search algorithm (IBSSA) to the problem model. Similar to this, [PERSON] et al. [22] applied the enhanced mayfly algorithm (GSWBMA) to the cognitive heterogeneous cellular network spectrum allocation problem, offering helpful guidance for future researchers. Numerous researchers have also enhanced numerous swarm intelligence algorithms in the context of traditional engineering applications [23; 24; 25]. A novel algorithm called the golden eagle optimizer (GEO) [26] was put forward in 2021, which was motivated by two hunting behaviors of golden eagles. The cruising and attacking phases are changed in order to catch prey more quickly. The shift from the golden eagle's cruise behavior to attack behavior has a direct impact on the end result. Cruise behavior mostly reflects the algorithm's exploration function, while attack behavior primarily reflects the algorithm's exploitation function. Because the original algorithm cannot balance the exploration and exploitation phases, it randomly switches from cruise behavior to attack behavior, which causes GEO to have a slow convergence speed, poor search accuracy, and weak robustness. There are some strategies to improve this. Few people, however, have previously used an improved approach to simultaneously take into account the cognitive radio allocation and traditional engineering design constraints, as described in this paper.