# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
###### Abstract
In multi-user detection, global search capability, local search capability, robustness, and adaptability are crucial for the performance of optimization algorithms. Having a good local search capability can quickly converge the algorithm after discovering the potential optimal solution to improve the accuracy and stability of the solution. Having a certain degree of adaptability can adapt the algorithm to changes in different channel environments and multi-user situations. The algorithm should have a strong global search capability, able to search the potential solution space extensively in the search space to avoid falling into local optimal solutions. The algorithm needs to be robust to channel noise and interference to ensure stability and reliability in complex channel environments. A new nature-inspired intelligent optimization algorithm, Cheetah Optimizer (CO) [16], was proposed in 2022 by [PERSON] et al. The algorithm is excellent and adaptable in local search, being successfully implemented in various engineering problems, including power detection [17], wireless transmission [18], multi-system exploration [19], natural resource detection [20], and renewable resource management [21]. However, the cheetah optimization algorithm has obvious problems of slow convergence and it is prone to being stuck in local optima when dealing with the multi-user detection problem. Differential Evolution (DE) [22] is a typical auxiliary algorithm that has the characteristics of high global search capability, strong robustness, etc., but has poor convergence accuracy in local search. Based on the highly complementary nature of the above two algorithms in coping with the multi-user detection problem, this paper considers the dynamic fusion of these two algorithms to deal with the multi-user detection problem. Firstly, according to the selection mechanism of the CO algorithm, the position update method is dynamically selected, and the contrarian learning strategy [23] is introduced to optimize the control parameters and individual update strategy of the CO algorithm to improve the searching accuracy. After that, the improved CO algorithm dynamically introduces the DE algorithm into the search process to improve the global search ability and convergence speed of the algorithm. Finally, a go-home strategy is introduced to return to the initial position to restart the next hunt if the leader fails to hunt or is unsuccessful in the hunting cycle. This improves the algorithm's search diversity, ensuring that it does not prematurely converge to incorrect positions during optimization, thereby enhancing the accuracy and reliability of the results. The meanings of abbreviations in the article can be found in Table 1. This paper's subsequent organization is as follows:  
Section 2.1 outlines the robust multi-user detection model, and Sections 2.2 and 2.3 briefly introduce the CO algorithm and DE algorithm. Sections 2.4 and 2.5 introduce the improved cheetah optimization algorithm strategy and the improved complete detection algorithm is analyzed and designed. Section 3 demonstrates the accuracy and reliability of the algorithm by introducing various intelligent optimization algorithms and comparing them with the proposed algorithm in terms of initialization parameter settings, convergence performance, anti-noise performance, resistance to near and far effects, and communication capacity.