Abstract: Flight conflict resolution is the key to control the air traffic flow and optimize flight scheduling. To enhance the search speed and avoid falling into the local optimization and premature convergence by Genetic Algorithm, a algorithm based on Genetic Algorithm and Particle Swarm algorithm is proposed to multi-vehicle confliction resolution. This algorithm integrates the global search capability of Genetic Algorithm and the memory function and fast convergence properties of Particle Swarm algorithm, which can effectively address the shortcomings of Genetic Algorithm. Experiments show the algorithm is able to draw good results and is better than Genetic Algorithm both in the search speed and search accuracy.

Key words: air traffic, multi-vehicle confliction resolution, Genetic Algorithm（GA）, Particle Swarm Optimization（PSO）

摘要： 飞行冲突解脱是空中交通流量控制与优化调度中的难点。针对遗传算法求解多机飞行冲突存在搜索速度慢、易陷入局部最优与早熟收敛的问题，提出一种遗传粒子群算法解决多机飞行冲突。该算法综合遗传算法的全局搜索能力和粒子群算法的记忆功能与快速收敛特性，能够有效地解决遗传算法求解飞行冲突存在的不足。仿真验证了该算法能够得出较好的结果，无论是在搜索速度还是在求解精度上都有明显的提高。

关键词: 空中交通, 多机冲突解脱, 遗传算法, 粒子群算法