# Improved Honey Badger Algorithm and Its Application to K-Means Clustering
###### Abstract
The algorithm builds a mathematical model based on honey badger's predatory behavior, which includes the behaviors of self-predation and cooperation with other organisms. The Honey Badger Algorithm has an extremely strong global search capability, which facilitates a seamless shift among searching on a global scale and searching locally with a short search time, and performs well in many application problems, especially in solving complex problems. Many researchers have enhanced the Honey Badger Algorithm and implemented it across various domains since its proposal thanks to its simple structure and fast search speed. [PERSON] et al. [20] proposed an improved HBA to optimize modeling proton exchange membrane fuel cells by using oppositional learning and chaotic mechanisms in the improved algorithm. [PERSON] et al. [21] used the HBA to solve the optimization of multi-objectives and applied the HBA to the integrated photovoltaic/extended infiltration/battery system optimization. [PERSON] et al. [22] proposed a clustering and routing planning scheme for wireless sensor networks based on the Honey Badger Algorithm. The newly proposed model has the longest lifetime compared to the existing techniques. [PERSON] et al. [23] combined HBA based on the set of good points with the Distance Vector-Hop (DV-Hop) algorithm to solve the problem of insufficient localization accuracy of the traditional DV-Hop algorithm and to prevent the algorithm from converging to a local optimum. [PERSON] et al. [24] added a segmental optimal decrement neighborhood and a horizontal hybridization strategy with strategic adaptability to HBA and applied the improved algorithm to unmanned aerial vehicle path planning to obtain a more feasible and efficient path. [PERSON] et al. [25] combined the advantages of HBA and the Artificial Gorilla Troops Optimizer to enhance the development ability of the algorithm, and the hybrid algorithm was applied to Bot problems to improve the performance and efficiency of the cloud computing system. [PERSON] et al. [26] proposed a microgrid energy management scheme based on HBA considering the advantages of HBA in addressing intricate challenges, and finally improved the operation efficiency of the microgrid. The Honey Badger Algorithm has inherent limitations, encompassing protracted convergence and a propensity to succumb to local optimal solutions [27]. Moreover, the applications of this algorithm in data clustering tasks are still relatively few, so this manuscript introduces a refined iteration of the Honey Badger Algorithm, termed the Improved Honey Badger Algorithm (IHBA). The IHBA is then applied to K-means clustering. The key contributions of this study can be summarized as follows:  
1. A sin chaotic strategy for honey badger populations is introduced to ensure that the population is uniformly distributed throughout the search space. 2. Improved density factor. This improvement accelerates the rate of convergence. 3. Nonlinear inertia weights are introduced in position updating, promoting a smooth transition in exploration and development. 4. Reverse learning of optimal individuals to increase population diversity and refine the algorithm to escape from local optima. 5. The enhanced algorithm is rigorously assessed through the application of 23 benchmark test functions. Additionally, the clustering analysis problem is addressed with the IHBA proposed in this study, which produces superior clustering results on three UCI test data sets. The organization of this paper is delineated as follows: Section 2 elucidates the foundational principles underlying the Honey Badger Algorithm. In Section 3, we present an enhanced strategy designed to rectify the inherent limitations associated with the Honey Badger Algorithm. Section 4 meticulously delineates the amalgamation of the K-means algorithm and the Honey Badger Algorithm. Section 5 presents a comprehensive analysis of the results derived from simulation experiments. Finally, Section 6 offers a succinct summary.