Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

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.

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

“…The cooperative path planning method for multiple UAVs in a deterministic environment can be regarded as an NP-hard combinatorial optimization problem with many constraints [10]. In recent years, many scholars have conducted extensive research on how to establish mathematical models and solve the NP-hard combinatorial optimization problem [11][12][13][14][15][16][17][18]. The main mathematical models include the multitrip salesman model (MTSP), mixed linear integer programming model (MILP), and vehicle scheduling and path planning model (VRP).…”

UAV Swarm Intelligence: Recent Advances and Future Trends

“…The UAV swarm can reach different places (marked and unmarked) in different situations and support emergency conditions in the city environment [60]. For the UAV path planning in the threat and confrontation area is a non-linear optimization problem with multiple static and dynamic constraints ， through a multi-drones collaborative path planning method on 3D rugged terrain multi-swarm fruit fly optimization algorithm (MSFOA), which solves the shortcomings of the original algorithm that the global convergence speed is too slow and the local optimization [61].…”

Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

“…At present, the main classical models for the multi-UAV cooperative reconnaissance problem include multitraveling salesman model (MTSP), mixed linear integer programming model (MILP), and vehicle scheduling and path planning model VRP [9]. ese models are solved by heuristic algorithms such as genetic algorithm [10], simulated annealing algorithm, and evolutionary algorithm [11]. However, the traditional model cannot completely describe the constraints of multi-UAV cooperative reconnaissance missions.…”