## 1 Introduction  
Unmanned aerial vehicles (UAVs) have become an area of great concern to many governmental and military organizations around the world. The autonomy level of the UAVs depends on the methodology used to control the vehicle and plan its flight path [1]. Therefore, path planning, which is to generate a feasible path between two points that has an optimal or near-optimal performance satisfying constraint conditions, is one of the most important challenges in the autonomous navigation process of UAVs [2]. Typically, in increasingly complex mission environments, only one UAV usually cannot meet the mission requirements, so there are often multiple UAVs (multi-UAVs) on the same mission at the same time [3]. This paper mainly discusses the path planning of multi-UAVs cooperation. To solve the cooperative path planning problem of multi-UAVs, an approach is proposed: Priority is set to each UAV. The first priority UAV does not need a plan. Its sub-goal is the final goal. The UAV with second priority plans the path to avoid collision with the first priority UAV. The UAV with the third priority needs to plan its path to avoid collision with the former UAVs [4]. It is proven that finding the optimal path is an non-deterministic polynomial complete problem [2]. As the scale of the problem increases, the complexity of the problem increases rapidly. In many existing works, the optimal path is always considered as the shortest path, and the deterministic search algorithm is used to find the shortest path. However, the optimal path of the UAV should be considered to be related to the minimum distance path, average height, fuel consumption, radar exposure, and so on, which make the problem more complex [5]. In order to reduce the computational complexity, many approaches have been applied to the path planning problem, which can be classified into traditional methods and bionic inspired algorithms. The traditional path planning methods contain the A* search algorithm [6], the polynomial optimization method [7], and artificial potential field based methods [8], while the bionic inspired algorithms contain the simulated annealing algorithm (SAA) [9], genetic algorithm (GA) [5], differential evolution (DE) [10], artificial bee colony (ABC) [11], particle swarm optimization (PSO) algorithm [12; 13], and ant colony optimization (ACO) [14]. Among these bionic inspired algorithms, ACO is usually applied to the combinational optimization problems [14; 15], while the others are usually used to solve the continuous optimization problems. As a kind of population-based random search algorithm, PSO has been widely applied to UAV path planning [16]. The basic idea of PSO comes from the behavior of birds that randomly search for food within an area. To simulate this situation, the particle swarm was developed as a useful computational technique for solving the optimization problem [17]. As a novel bio-inspired technique, the fruit fly optimization algorithm (FOA) was first proposed by [PERSON] in 2011 [18], inspired by the food finding behavior of fruit flies. FOA is well-known for its simple principle, few adjustable parameters, strong global optimization capability, and fast convergence speed [19]. So far, FOA has been used in a variety of applications, such as general regression neural network parameters optimization [20; 21] and the tuning of controller parameters [22]. Several improved versions of FOA were proposed by researchers to enhance the search efficiency and global search ability of the basic FOA; these include the multi-swarm fruit fly optimization algorithm (MFOA) [23] and improved fruit fly optimization (IFFO) [24], to name a few. In this paper, the new multiple swarm fruit fly optimization algorithm (MSFOA) is proposed. In the MSFOA, the whole fruit fly swarm are divided into several sub-swarms with multi-tasks. In the early searching stage, the sub-swarms are inclined to global searching, while at the end of the searching stage, they are inclined to local searching.