PSO-based multi UCAVs cooperative attack tasks allocation and its simulation
Xiaojun Xing, Dongsheng Fan, Yaqing Zhao, Longliang Huang · 2016
Task allocation for Multi UCAVs (unmanned combat aerial vehicle) cooperatively attacking targets is a complex NP-hard nonlinear multiple objective optimization problem with strong constraints. A new method for solving the problem is proposed based on an improved particle swarm optimization (PSO) algorithm which has parallelism, high resolution and high efficiency. Firstly, an improved PSO other than basic classical PSO is presented, of which, the whole particle swarm is split into several sub-swarms, and each sub-swarm evolves respectively in the first stage, afterwards each sub-swarm was emerged into one swarm and evolves in the second stage. Secondly, the improved PSO is applied to solve the multi-UCAVs attack task allocation problem. Simulation results show that the algorithm is better and more precise in UCAVs cooperative attack path.