## 1 Introduction  
In recent years, with the rapid development of science and technology, modern battlefield military equipment has shown a clear trend toward unmanned operation. As an important means of airborne reconnaissance, surveillance and combat, the aircraft is increasingly a primary concerned of militaries around the world. However, with the increasing complexity of the modern battlefield environment and the continuous expansion of the scope of operation, UCAV not only need to avoid or reduce the probability of detection, but also avoid many adverse factors that may affect the flight in no-fly zones and barrier regions, which have brought serious challenges to the implementation of aerial reconnaissance, surveillance, combat and other missions to the UCAV. Therefore, in order to improve the operational efficiency and the survival probability, path planning must take into account the requirements of the task, the threat distribution, the fuel restriction and other constraints, when producing a global optimal or sub-optimal route that can effectively avoid the threat of an enemy and protect the UCAV. Furthermore, a path-planning algorithm must be able to adjust and modify the route according to changes in the battlefield. At present, in military and civilian fields, the UCAV path-planning problem has been widely studied. Many heuristic authors have proposed algorithms have been used to solve the problem, which have achieved good results [PERSON] _et al._ proposed a particle swarm optimization based on second-order oscillating (SOPSO) to solve the problem [1]. [PERSON] _et al._ proposed the path planning method based on artificial fish school algorithm (AFSA) to solve UCAV path-planning problem [2]. [PERSON] _et al._ applied differential evolution (DE) to solve the problem [3]. [PERSON] _et al._ proposed a bat algorithm with mutation (BAM) for solving the UCAV path-planning problem [4]. [PERSON] _et al._ proposed a new modified firefly algorithm (MFA) based on a modification in exchange information to solve the UCAV path planning problem [5]. [PERSON] _et al._ proposed a novel artificial bee colony algorithm (ABC) improved by a balance-evolution strategy to solve the problem [6]. [PERSON] _et al._ proposed a wolf colony search algorithm (WCA) based on the complex method to solve the UCAV path planning problem [7]. [PERSON] _et al._ proposed a novel Chaotic Predator-Prey Biogeography-Based Optimization (CPBBO) approach based on the chaos theory and the concept of predator-prey for solving UCAV path planning problem [8]. The wind driven optimization (WDO) is a novel nature-inspired technique that was proposed by [PERSON] _et al._ in 2010 [9, 10]. In the atmosphere, wind balances atmospheric pressure through flow. Wind flows from high pressure to low pressure at a certain speed until a balance point is reached. Because the WDO algorithm has only a few parameters that need to be controlled, and it is very easy to implement, it has received much attention by various scholars since it was put forward. In recent years, the WDO algorithm has also been applied in many fields, for instance, in satellite image segmentation for multilevel thresholding [11], cloud resource allocation scheme [12], collision avoidance for dynamic environments [13], design of two-channel filter bank [14], synthesis of linear array antenna [15], and so on. In the classical natural heuristic algorithm, the population an individual uses is the real number encoding or the binary encoding [16]. In the quantum-inspired algorithm, the individual is represented by a quantum bit. The probability amplitude of the qubit should be used for the individual, so that each individual can be represented by a superposition of multiple states [17]. As a result, quantum-inspiredalgorithms have better population diversity, faster convergence speed, and better global optimization ability than traditional heuristic algorithms.