基于进化计算的无人飞行器多航迹规划

针对飞行器多航迹规划问题展开研究。在分析多峰值函数优化问题的基础上，提出了一种基于进化计算的飞行器多航迹规划方法。该方法通过使用特定的染色体表示方法和进化算子，可以有效利用各种环境信息，处理各种航迹约束。同时，通过引入聚类算法，将种群中的个体按其空间分布进行聚类，生成若干个不同子种群。在进化过程中，所有个体只在各自的子种群内部进化。当进化结束时，每个子种群将分别生成一条各自的最优航迹，从而为飞行器生成多条不同的可选航迹。仿真结果表明了该方法的有效性。

Multiple Routes Planning for Unmanned Air Vehicle Based on Evolutionary Computation

The multiple routes planning problem of UAVs is studied. Based on the analysis of multi-modal function optimization using evolutionary computation, a new multiple routes planner for unmanned air vehicles is proposed. With a problem-specific real-value representation of candidate solutions and genetic operators, the algorithm can take into account different kinds of environmental information, and handle various mission constraints. By introducing the clustering technical, the individuals of the population are clustered according their distribution in the space, and form a number of sub-populations. In the evolutionary process, all routes evolve only in their own sub-population. At the end of the evolution, each sub-population provides an optimal route, and thus the algorithm generates multiple routes for the vehicle. The simulation results demonstrated the feasibility of the approach.