# A Multi-Strategy Improved Honey Badger Algorithm for Engineering Design Problems
###### Abstract
honey badger algorithm (HBA), as an emerging optimization algorithm, provides new ideas for solving engineering design problems with its unique search mechanism and powerful global search capability [21]. [PERSON] et al. optimized HNGS, a hybrid nano grid system for the future residential area of Mahendragarh, Haryana, India, using honey badger algorithm (HBA) and obtained a higher quality solution [22]. [PERSON] et al. combined the HBA with a gradient boosting decision tree (GBDT) to improve the prediction accuracy of the control parameters and to realize the real-time adjustment of the parameters to meet the changes in the grid demand [23]. [PERSON] et al. proposed an optimization framework based on binarized spiking neural network (BzSpNN) and honey badger algorithm for cluster head selection in WSNs to minimize the energy consumption and extend the network life cycle [24]. [PERSON] et al. proposed a combined method (CGH-GTO), based on the improved gray wolf optimizer (IGWO), honey badger algorithm (HBA), and gorilla troops optimizer (GTO), for the identification of PV model parameters [25]. [PERSON] et al. constructed a two-layer planning model for combined cooling, heating and power (CCHP) and developed the multi-objective honey badger algorithm (MOHBA), which improves the economic, energy, and environmental performance of the CCHP system [26]. [PERSON] et al. proposed an improved binary honey badger algorithm for IoT traffic datasets based on levy flights, which improves the performance of HBA for IoT device identification in the feature selection problem [27]. [PERSON] et al. proposed \(\alpha\)4 CycoHBA, an algorithm that significantly improves the global search capability and convergence speed by introducing density factors based on primitive functions and mathematical spirals in polar coordinate systems [28]. [PERSON] et al. proposed an improved honey badger algorithm called ODEHBA, which excels in solving complex numerical problems, engineering design problems, and routing problems for vehicular networking [29]. [PERSON] et al. proposed a back propagation neural network (BPNN) model based on HBA optimization for predicting the migration time of toxic fumes induced by blasting in underground mines to further guide and improve the ventilation design [30]. The honey badger algorithm excels in solving optimization problems with its simplicity and efficiency, and is especially suitable for complex and high-dimensional search spaces. Although the HBA shows some advantages in optimization problems with its simple structure and clear ideas, it also has some drawbacks. The HBA is weak in exploration capability, which leads to the algorithm's difficulty in effectively exploring the solution space at the early stage of the search, thus affecting the ability of global optimization search. The algorithm does not handle the balance between exploration and exploitation well enough, which modifies the convergence performance of the algorithm and the quality of the final solution. The HBA also tends to fall into local optimal solutions, and it requires more iterations to achieve better optimization results, which is a challenge to the computational resources. In order to better solve the engineering design problem, this paper improves the original HBA. In this paper, we propose an improved honey badger algorithm (MIHBA) based on multiple strategies to address the shortcomings of the honey badger algorithm. The introduction of Halton sequence to initialize the population enables the generation of point sets that are uniformly distributed in multidimensional space. Initializing the population with Halton sequence can improve the diversity of the algorithm and avoid premature convergence. The dynamic density factor of water waves is introduced to perform an efficient search in the solution space by modeling the propagation, refraction, and breaking wave motion mechanisms of water waves.