Abstract
The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.
Highlights
Unmanned aerial vehicles (UAVs) have become an area of great concern to many governmental and military organizations around the world
This section discusses the simulations used to assess the performance of our proposed algorithm in solving the UAV path planning problem
The 3D stereo displays of the seven UAV paths in the three scenarios are shown in Figures 2–4, where (a)–(g) correspond to multiple swarm fruit fly optimization algorithm (MSFOA), fly optimization algorithm (FOA), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), improved fruit fly optimization (IFFO), and multi-swarm fruit fly optimization algorithm (MFOA), respectively
Summary
Unmanned aerial vehicles (UAVs) have become an area of great concern to many governmental and military organizations around the world. 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]. A collision detection method is proposed to avoid collision among multi-UAVs. The simulation results show that the proposed MSFOA-based path planner can effectively solve the coordinated path planning problem among multi-UAVs. The rest of the paper is arranged as follows: The Section 2 introduces the basic FOA technique.
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