# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 1 Introduction  
With the rapid development of various industries, many optimization problems arise in real life. Real-world optimization problems are usually very complex and computationally difficult [1, 2]. To address these challenges effectively, metaheuristic algorithms are applied. Significant progress has been made in metaheuristic algorithms in recent years. These algorithms can be broadly categorized into two types: population-based algorithms and evolution-based algorithms [3]. Evolution-based algorithms are inspired by biological evolution principles. Genetic Algorithm (GA) [4] is a typical example of this theory. Similarly, the widely studied Differential Evolution (DE) [5] simulates natural processes like survival of the fittest and biological evolution. Population-based algorithms provide solutions by distributing information about all participants in the optimization. One of the better such algorithms in recent years is the Sparrow Search Algorithm (SSA) proposed by [PERSON] et al., which simulates the behavior of sparrows while foraging and avoiding predators [6]. By simulating the foraging and storage behaviors of starlings in summer and fall as well as the memory-based food-seeking behaviors in winter and spring, [PERSON] et al. proposed the Nutcracker Optimization Algorithm (NOA) [7]. [PERSON] et al. proposed the Black-winged Kite Algorithm (BKA) in 2024 [8], inspired by black-winged kite migration and foraging behaviors. And other similar algorithms include Wild Horse Optimizer (WHO) [9], GoldenJackal Optimization (GJO) [10], Sea-horse Optimizer (SHO) [11], the Greater Cane Rat Algorithm (GCRA) [12], and Red-billed Blue Magpie Optimizer (RBMO) [13]. Based on the better performance of these algorithms, many scholars have improved the basic algorithms and proposed some improved algorithms with better results. [PERSON] et al. integrated the sine-cosine algorithm and the Cauchy variational strategy into SSA to obtain SCSSA [14]. Inspired by the techniques of opposition-based learning (OBL) and randomized opposition-based learning (ROBL), [PERSON] proposed a fast randomized opposition-based learning Golden Jaguar optimization algorithm (FROBL-GJO) [15] to improve the GJO algorithm's optimization accuracy and convergence speed. [PERSON] et al. combined the sine-cosine algorithm with the hippocampus optimization algorithm to obtain the Sea-horse Optimizer Sine Cosine Algorithm (SHOSCA) [16]. The improvement of metaheuristic algorithms is not only important in the theoretical setting, but also crucial in practical applications. Efficient path planning can help robots accomplish tasks better and reduce the waste of resources. [PERSON] et al. proposed the Elite Preserving Differential Evolutionary Gray Wolf Optimizer (EPDE-GWO) [17] in order to optimize the Vertical Farming System Multi-Robot Trajectory Planning Model (VFSMRTP). In order to optimize the path planning of warehouse logistics robots and improve the shortest route and smoothness of the path, [PERSON] et al. proposed the multi-strategy Integrated Whale Algorithm (IWOA) [18]. [PERSON] applied the Gray Wolf Optimizer to the Constrained Optimization Problem for a line-start PM Motor [19]. [PERSON] et al. proposed a multi-strategy improved Harris Hawk optimization algorithm (MIHHO) [20]. This algorithm aims to overcome challenges in indoor mobile robot path planning, such as local shortest paths, slow search speed, and low path accuracy. The path planning problem must consider path length, computation time, and path feasibility [21]. Traditional metaheuristic algorithms do not take these aspects into account at the same time, and it is difficult to find a balanced solution. So, this study is challenging. In 2022, [PERSON] et al. proposed the Dung Beetle Optimization Algorithm (DBO) [22]. Like many other metaheuristic algorithms, DBO faces challenges such as slow convergence speed, low convergence accuracy, and a tendency to get stuck in local optima.