# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 2 Related Works and Method  
### Related Works  
Scholars have used the DBO algorithm in practical problems. In a previous study [23], the DBO algorithm was employed for photovoltaic array fault diagnosis. It was integrated with Long Short-Term Memory (LSTM) networks to construct an effective fault diagnosis model. The authors of [24] proposed an enhanced multi-strategy dung beetle algorithm, and the improved algorithm was used for trajectory planning in a 3D environment; the results showed that the paths can be planned to avoid hazardous sources in a safer and more effective way. The authors of [25] proposed the MIDBO algorithm; experiments demonstrated that the MIDBO method outperforms the alternative approach.  
In order to use robots in real life, the robot path planning problem is fundamental. The essence of this is to plan a feasible path for the robot that connects the starting point to the end point and satisfies the actual constraints. Traditional path planning methods mainly include the Dijkstra algorithm [26], A* algorithm [27], artificial potential field method [28], RRT [29], etc. However, these traditional methods face limitations in dynamic or complex environments. For example, they struggle with generating smooth paths and meeting real-time response requirements. Additionally, their performance is constrained in high-dimensional spaces or nonlinear optimization problems, making them less suitable for path planning in intricate environments [30]. The emergence of metaheuristic algorithms provided a new solution to this problem. In recent years, metaheuristic algorithms have stood out for their better robustness, global search capability, and adaptability [31].  
Traditional metaheuristic algorithms for path planning include particle swarm optimization (PSO) [32], ant colony optimization (ACO) [33], the firefly algorithm (FA) [34], etc. PSO has a faster convergence speed, but while working with complicated surroundings and multi-peak situations, the performance is unsatisfactory. ACO has stronger global search capability [35], but has high computational complexity and a slower convergence speed [36; 37]. FA has powerful diversified search capability, but is not adaptable enough in dealing with complex environments. Although these algorithms are capable of global search and path optimization, they are often limited in their ability to efficiently handle complex problems, plan paths effectively, and minimize computational overhead. TS-DBO improved the algorithm's flexibility in intricate settings and its ability to satisfy path planning requirements.  
The DBO algorithm demonstrates strong optimization capabilities and fast convergence, inspired by the dung beetle's natural foraging behavior. However, it faces several challenges, such as an uneven distribution of the initial population, an imbalance between local exploitation and global exploration, and a tendency to get trapped in local optima. Despite these limitations, the biologically inspired search process of DBO has garnered significant attention in the field of robot path planning. However, poor convergence, limited adaptation to dynamic impediments, and the inability to update pathways in real time are some of the problems that traditional DBO faces [38]. Nevertheless, it is a strong contender for path planning applications due to its capacity for global search and path optimization.