基于GABC算法的作战体系智能优化方法研究

为解决探索性仿真无法快速地遍历解空间，实时提供辅助决策方案的问题，提出了基于分类器的遗传算法，建立了基于该算法的作战体系仿真优化方法框架，能够根据体系关键因素和决策目标的动态变化寻找最优解，适用于诸如寻求最佳效费比方案、最优力量部署等多种体系优化问题。基于国防大学的仿真试验床系统进行了某海域火力拦阻作战的实验，通过GABC(genetic algorithm based on classifier)算法优化了力量配置问题。实验表明，该方法可以准确、快速地从复杂的体系限制条件中寻找效费比最高的方案，可以大量减少仿真实验的次数，满足快速辅助指挥员决策的需求。

Research on Intelligent Optimization Method of Combat SoS Based on GABC Algorithm

In order to solve the problem that exploratory simulation can not traverse the solution space quickly, and provide the auxiliary decision-making scheme in real time, a genetic algorithm based on classifier is proposed. The framework of simulation optimization method based on the algorithm is established. It can find the optimal solution according to the dynamic changes of key factors and decision targets of the system, which is suitable for such as seeking the best efficiency-cost ratio scheme and the optimization of the optimal power deployment and other systems. Based on the simulation bed system of the National Defense University, experiments on fire blocking operations in a sea area are carried out. The power allocation problem is optimized by GABC (genetic algorithm based on classifier). The experiment shows that the method can find the most efficient and cost ratio scheme from the complex system constraints accurately and quickly, and can reduce the number of simulation experiments and meet the needs of quick assistant commander decision.