Application of normal cloud based adaptive genetic algorithm in UAV path planning

Sequential genetic algorithm(SGA) easily gets stuck at a local optimum and has a slow convergent speed.To overcome its shortage,this paper presented NCAGA for UAV path planning.It applied a novel method of encoding based on rectangular plane coordinate system,which simplified the complexity of encoding and achieved a higher planning speed.The improved genetic algorithm combined with the normal X-condition cloud generator to adjust the probability of crossover and mutation adaptively.The stable tendency of normal cloud contributed a higher convergence speed and character of randomness conduced to a lower possibility of premature.Simulation results demonstrate that NCAGA is able to plan path quickly that made UAV avoid the dangerous areas with a higher effectiveness and success rate,so that it has a wide application prospect.