防空C^3I目标分配问题的ACO-SA混合优化策略研究

Research of ACO-SA optimization strategy for solving target assignment problem in air-defense C^3I system

防空C3I系统的目标分配已成为现代防空作战指挥不可缺少的决策支持,针对这一问题,提出了蚁群-模拟退火(ACO-SA)混合优化策略。在该策略中,蚁群系统的一次周游过程中的最优路线作为模拟退火算法的初始解,在每个退火温度上进行抽样准则检验并产生新解,然后更新新解对应路径上的信息素,蚁群算法(ACO)再根据新的信息素分布进行并行搜索。实验表明,与单一ACO和SA算法相比,这种ACO-SA混合优化策略在解决同一防空C3I系统的目标分配问题上有较强的寻优能力和较快的收敛速度。

Targets assignment of an air-defense C^3I system is a key decision-making support in modern air-defense fighting. To solve the targets assignment problem, a hybrid optimization strategy with ACO and SA is presented. In the hybrid strategy, a cycle course of the ant system can provide current best solution as effective initial solution for SA, and SA generates a new solution based on the metropolis criterion at each temperature, then the ant system updates pheromone trails and proceeds with parallel searching through reusing the new solution from SA. Test shows that the performance of this optimization method is better on finding optimal solution and quick convergence than the single ACO or SA in solving the same targets assignment problem.