## 1 Introduction
[PERSON], [PERSON], and [PERSON] introduced an adaptive selection mutation-constrained differential evolution algorithm, which shows promising application prospects in disaster scenarios [20]. [PERSON], [PERSON], and [PERSON] developed a quantum theory-improved firefly algorithm (QFOA), utilizing wave functions to replace random search, thereby enhancing population diversity and outperforming the traditional firefly algorithm (FQA) in terms of search capability, stability, and robustness [21]. The combination of simplified grey wolf optimization (SGWO) and the mutual symbiotic organism search (MSOS) algorithm has also demonstrated potential in UAV path planning [22]. To improve path planning efficiency and ensure the smoothness and safety of UAV operations, [PERSON], [PERSON], and [PERSON] proposed a hybrid algorithm that integrates grey wolf optimization (GWO) with the artificial potential field method (APF), significantly enhancing path planning capabilities, particularly in scenarios with short distances and high safety requirements [23]. Despite the widespread application of intelligent algorithms and neural networks in UAV path planning, most algorithms remain limited to two-dimensional path planning, failing to adequately consider factors such as obstacle height and flight slope, while some methods exhibit high computational complexity. The state transition simulated annealing algorithm (STASA), a hybrid intelligent optimization algorithm proposed by [PERSON] et al. [24]. It combines the strengths of the state transition algorithm (STA) [25] and simulated annealing (SA) [26], achieving significant results in various application areas, such as PM2.5 prediction [27] and the optimization of methane production conditions in coal-to-gas processes [28]. Compared to traditional algorithms such as genetic algorithm (GA) [29], particle swarm optimization (PSO) [30], and grey wolf optimizer (GWO) [31], STASA demonstrates greater efficiency and flexibility. STASA offers greater efficiency and flexibility. However, its performance declines when addressing complex, high-dimensional problems. To address the shortcomings of the state transition simulated annealing algorithm (STASA), this paper introduces the state transition simulated annealing algorithm with destructive perturbation operators and reverse learning strategies (DRSTASA). This algorithm enhances population diversity using Latin hypercube sampling and incorporates destructive perturbation and reverse learning strategies to improve global search, speed up convergence, and avoid local optima. Experimental results show that DRSTASA outperforms other algorithms on 23 benchmark test functions and 8 engineering optimization problems. It has also been successfully applied to three-dimensional UAV path planning, demonstrating strong practical feasibility [32]. The rest of this paper is structured as follows: Section 2 covers the objective function for UAV path planning, Section 3 explains the original STASA algorithm, Section 4 describes the enhancements made by DRSTASA, and Section 5 presents a comparison with other algorithms and experimental results. Finally, a summary is provided.