## 1 Introduction  
In recent years, the demand for unmanned logistics has increased significantly. As key logistics equipment, unmanned delivery vehicles play a vital role in warehousing, sorting, and transportation processes [1]. Path planning, a core issue [2, 3], directly impacts operational efficiency and task completion quality. Researchers worldwide have extensively studied path planning, achieving notable progress through algorithms ranging from traditional methods like the A* algorithm [4, 5] (A-star algorithm), artificial potential field method [6], and [PERSON]'s algorithm [7], for its improvement, and intelligent algorithms such as simulated annealing [8], ant colony optimization [9], genetic algorithms, and grey wolf optimization. However, traditional algorithms struggle in complex environments with multiple obstacles, resulting in poor timeliness and lengthy paths, which hinder unmanned vehicle operations. To address this, metaheuristic algorithms inspired by natural biological systems have gained attention for their efficiency [10, 11]. These algorithms, mimicking collective behavior or ecological mechanisms, excel in optimization tasks. Among them, genetic algorithms were among the first applied to combinatorial optimization problems and are widely used in robotic path planning [12]. Recently, the Grey Wolf Optimizer (GWO) [13] has become a prominent metaheuristic algorithm for various optimization problems. [PERSON] et al. [14] improved GWO by introducing weight factors and displacement vectors to enhance exploration strategies and avoid infeasible solutions. [PERSON] et al. [15] tackled local optima by incorporating mutation methods to control mutation rates effectively. The Chimp Optimization Algorithm (ChOA) is a novel metaheuristic inspired by chimpanzee population behavior [16], combining the strengths of the Grey Wolf Optimization Algorithm, Dragonfly Algorithm, and Whale Optimization Algorithm. It features strong global search capability, adaptability, and fewer control parameters. However, traditional ChOA suffers from slow convergence and low accuracy. To address these issues, [PERSON] et al. (2021) proposed a weighted chimpanzee algorithm, improving convergence speed and mitigating local optima capture in high-dimensional optimization problems, validated through 13 high-dimensional and 10 real-world optimization cases [17]. The weighted Chimp Optimization Algorithm has shown improvements in high-dimensional optimization through a weighting mechanism. However, it still encounters common issues found in classic heuristic optimization algorithms, such as slow convergence, susceptibility to local optima, and high computational resource demands. In 2022, [PERSON] et al. combined Particle Swarm Optimization (PSO) local search techniques with a new constraint handling method, ensuring robustness in photovoltaic model parameter estimation optimization [18]. Despite its versatility, PSO often falls into local optima, particularly in complex or high-dimensional problems, limiting its global search capability. To address these issues, [PERSON] et al. (2022) introduced six spiral functions and proposed two new hybrid spiral functions into the ChOA algorithm, aiming to mitigate slow search speeds and premature convergence [19]. However, spiral functions require precise parameter tuning to remain effective across different problem domains. [PERSON] et al. also identified limitations of ChOA in handling binary problems and developed a binary version, BChOA, using four S- and V-shaped transfer functions alongside a new binary method [20]. Comparisons of the four new binary optimization algorithms with 18 state-of-the-art methods revealed that BChOA, supported by V-shaped transfer functions, achieved significant statistical improvements. However, BChOA is tailored for binary problems rather than continuous ones, limiting its applicability. While it performs well in specific tasks, its generalization ability may be insufficient. In 2024, [PERSON] et al.