国家自然科学基金青年基金(61902288); 国家自然科学基金(61672401); 国家重点研发计划(2019YFB1406404); 陕西省重点研发计划(908014487064)
指挥控制信息系统(指控系统)运行在动态变化的复杂环境中且任务需求时刻变更, 亟需一种自适应决策方法以动态产生调整系统的最优策略, 从而适应环境或任务变化, 确保系统长期稳定运行. 随着指控系统自身及其运行环境的持续复杂化, 自适应决策方法需具备应对多个非预期变化的在线权衡决策能力, 以避免造成冲突的调整后果或无法及时响应未知情况. 然而, 当前指控系统多采用基于先验知识、应对单一变化的自适应决策方法, 尚无法完全满足该能力需求. 因此, 提出了一种基于并行搜索优化的指控系统自适应决策方法. 方法采用基于搜索的软件工程思想, 将自适应决策问题建模为搜索优化问题, 并采用遗传粒子群算法, 实现针对同时发生的多个变化进行在线权衡的目标. 并且, 为解决该方法在指控系统中实际应用时存在的搜索效率保障、策略择优选择问题, 分别采用并行遗传算法和后优化理论, 对决策方法实现了并行化并建立了策略多指标排序法, 以确保方法的实用性.
The command and control information system (command and control system) runs in a dynamically changing and complex environment with constantly changed mission requirements. A self-adaptation decision-making method is urgently needed to dynamically generate the optimal strategy for adjusting the system, so as to adapt to changes in the environment or missions and ensure the long-term stable operation. At present, as the command and control system itself and its operating environment continue to become more complex, self-adaptation decision-making methods need to have the online trade-off decision-making ability to deal with multiple unexpected changes, so as to avoid conflicting adjustment consequences or failure to respond to unknown situations in a timely manner. Nevertheless, the current command and control system mostly adopts self-adaptation decision-making methods based on prior knowledge and responding to single changes, which cannot fully meet this capability requirement. Therefore, this study proposes a self-adaptation decision-making method for the command and control system based on parallel search optimization. This method uses search-based software engineering ideas to model the self-adaptation decision-making problem as a search optimization problem, and uses the genetic particle swarm algorithm to achieve the goal of online weighing against multiple changes that occur at the same time. In addition, in order to solve the problems of search efficiency guarantee and strategy selection in the actual application of this method in the command and control system, this study uses parallel genetic algorithm and POST-optimization theory to parallelize the self-adaptation decision-making method and establish a strategy multi-index sorting method to ensure the practicality of the method.