# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 1 Introduction
These algorithms operate by following explicit rules and logical reasoning to find paths; however, they may encounter challenges related to local optima and memory overflow when dealing with complex environments. On the other hand, AI-based path-planning methods leverage concepts from machine learning to tackle these problems, primarily through reinforcement learning and swarm intelligence optimization algorithms [13]. Reinforcement learning methods rely on continuous interactions between agents and their environment, improving decision-making strategies to maximize cumulative rewards and identify the optimal paths from start to finish [14]. However, reinforcement learning algorithms often require a large number of training samples,  
Figure 1: Drone–based maritime search and rescue process. which may lead to limitations such as prolonged training time and low sample efficiency in practical applications. Swarm intelligence algorithms simulate the behavior of biological groups found in nature by utilizing local interactions among simple agents that generate complex global intelligent behavior [15; 16]. The advantages of these methods lie in their ability to achieve global search capability, high adaptability, strong robustness, and ease of parallelization in multi-core processors and distributed computing environments, thereby accelerating path planning. In recent years, remarkable progress has been made by numerous researchers in improving UAV path planning through the use of swarm intelligence optimization algorithms. For instance, [PERSON] et al. proposed a multi-strategy balanced progressive learning Sparrow Search Algorithm (SSA) [17], which introduced balanced progressive learning and spiral progressive learning strategies to augment optimization performance, and successfully applied it to path-planning problems of varying complexities. However, when applied to high-dimensional complex environments, the algorithm's computational complexity experiences a significant increase, leading to a slower convergence speed. [PERSON] et al. developed a Grey Wolf Optimization (GWO) algorithm enhanced with reinforcement learning [18], which adaptively controlled individual switching operations based on accumulated performance, thereby improving efficiency in trajectory planning optimization. Although this algorithm improved efficiency in optimization tasks, its global search capability still requires enhancement when confronted with intricate environments. [PERSON], X.et al. [19] proposed a DE-GWO hybrid algorithm integrated with mutation factors for UAV path planning within complex environments that significantly amplified global search capability; however, there is room for improvement regarding local exploitation capabilities to ensure adaptability even under corner case scenarios. [PERSON] et al. [20] developed an improved Honey Badger Algorithm based on the Bernoulli mapping technique along with segmented optimal decreasing neighborhoods and strategy-adaptive horizontal crossover approach to address UAV path planning predicaments grounded on threat models. Although this algorithm has demonstrated satisfactory performance in scenarios involving spherical obstacles, there is still potential to improve its computational efficiency and path smoothness when handling more complex obstacle environments. [PERSON] et al. proposed an improved GWO algorithm [21], which introduces a spiral position update method, sets the update probability according to the golden ratio, and incorporates an adaptive leadership hierarchy to enhance convergence speed and task completion efficiency. Nevertheless, the algorithm's ability to escape from local optima remains weak. Despite significant progress in optimizing UAV path planning, research specific to maritime rescue scenarios remains scarce. Furthermore, existing algorithms continue to face challenges such as being trapped in local optima, having a slow convergence rate, and high computational complexity.