采用病毒粒子群优化算法的飞行器航迹规划

Aircraft Path Planning Based on Virus-Evolutionary Particle Swarm Optimization Algorithm

针对飞行器航迹规划问题,对基本粒子群算法进行了改进,提出一种基于病毒粒子群优化算法的飞行器航迹规划方法。该方法结合生物病毒进化系统理论,在基本粒子群算法中引入病毒种群,通过执行反向代换和转导算子两种操作,利用病毒的水平感染和垂直传播能力来维持主群体粒子种群和病毒种群之间的信息交换,保证了航迹规划中粒子个体的多样性,提高了算法的局部搜索能力,解决了基本粒子群优化算法容易使粒子陷入局部最优、收敛速度慢的问题。仿真实验结果表明,在相同的约束条件下,所提出的方法能够更快更有效地生成满足要求的航迹。

Aiming at the path planning problem of aerial vehicles with unexpected threats,a virus-evolutionary particle swarm optimization algorithm （VEPSO） is proposed based on basic particle swarm algorithm .Combined with the theory of the biological virus-evolutionary system,a biological virus mechanism is introduced in the basic particle swarm,and two kinds of infection-based operation,reverse substitution and incorporation operation,are executed .The horizontal infection and vertical propagation of virus are used to maintain the individual diversity of the particles in path planning,thus the local search capability of the algorithm is enhanced,and the problems of local optimum and the slow convergence speed are solved .Simulation results show that the VEPSO algorithm is faster and more effective under the same path constraints .