## 1 Introduction  
Path planning is a key technology for mobile platforms to realize intelligent functions such as environmental sensing and autonomous operation. Algorithms are utilized to enable mobile platforms to find optimal feasible paths from the initial point to the target location in different spaces, thereby improving operational efficiency [1]. Path planning algorithms can be broadly categorized into two classes: traditional algorithms and bionic intelligence-based algorithms [2], and they are widely used in fields such as electric power inspection [3], underwater search [4], and military task assignment [5]. Traditional algorithms can be subdivided into global and local path planning algorithms according to the utilization degree of environmental information. Global path planning provides approximate paths, while local path planning makes corrections within a local range of the global path to adapt to environmental changes and special needs. The global path planning algorithm needs to consider obstacle information in the environmental map, with representative algorithms including the A\({}^{*}\) and RRT algorithms [6]; the local path planning algorithm needs to update real-time environmental information via sensors, with representative algorithms such as the artificial potential field method and D\({}^{*}\) algorithm [7]. With the development of artificial intelligence technologies, bionic swarm intelligence algorithms that simulate biological evolution, group social behavior, and information exchange in nature have been formulated using mathematical expressions [8]. Early representative algorithms of swarm intelligence algorithms are particle swarm optimization (PSO) and ant colony optimization (ACO) [9]; medium-term representative algorithms are artificial bee colony (ABC) and artificial fish swarm algorithm (AFSA) [10]; and recent representative algorithms are sparrow search algorithm (SSA), whale optimization algorithm (WOA), and beetle antennae search (BAS), etc. [11]. The swarm intelligence algorithms have independent solution thinking and they can be adjusted adaptively and iteratively according to the path planning characteristics and requirements. Rich population categories help to achieve parallel computing and improve search efficiency while also having excellent scalability for larger-scale and more complex scenarios [12]. Swarm intelligence algorithms include two phases: global exploration and local optimization. The former performs a highly stochastic global search, while the latter performs deep optimization as the number of iterations in the search area increases. Regulating the balance between these two phases is particularly important. If the weight in the early exploration stage is too large, the algorithm may generate an unordered search, which slows down the convergence speed and wastes computing resources. If the weight in the later stage is too large, the algorithm may fall into local optimal oscillations and be unable to escape extreme points in the later stage [13]. The swarm intelligence algorithms do not rely on the initial parameter settings, and they are more inclined to explore the environmental information with a wider search range. The beetle algorithm has a high degree of structuring. It can not only simulate an individual beetle to judge the information concentration in the environment and solve problems independently but also be converted into a population of beetles to establish information interaction for a common solution. Some scholars combined the powerful search ability of the swarm algorithm with the low complexity of the beetle algorithm to improve the global search ability and local planning speed of the algorithm [14]. [PERSON] et al. [15] introduced globally optimal individuals into both the employed bees and scout bees stages and introduced the teaching- and learning-based optimization algorithm into the peak-peak stage to enhance the algorithm's mining ability and searching efficiency in terms of swarm population. [PERSON] et al.