基于改进灰狼算法优化SVR的航天侦察装备效能评估

定量评估航天侦察装备效能是武器装备体系建设的重要环节之一, 对装备发展和作战应用具有重要的现实意义。针对评估样本数据少、效能在多指标因素影响下变化规律非线性等条件下的效能评估问题, 提出一种基于改进灰狼(improved grey wolf optimizer, IGWO)算法优化的支持向量回归机(support vector regression, SVR)评估方法(IGWO-SVR)。引入反向学习策略及余弦非线性收敛因子改进灰狼优化算法收敛性能及全局寻优能力, 并将其应用于基于支持SVR效能评估参数的优化。基于航天侦察装备特点, 构建评估指标体系及航天侦察装备效能评估模型。最后, 通过对一定作战想定背景下航天侦察装备效能进行仿真评估, 验证了所提方法的合理性及优化模型的有效性。

Operational effectiveness evaluation of space reconnaissance equipment based on SVR optimized by improved grey wolf optimizer

Quantitative evaluation of operational effectiveness is an important part of reconnaissance satellite system (RSS) construction and has important practical significance for its development and combat application. In view of the problems such as small number of evaluation sample data and nonlinear change of performance under the influence of multiple index factors, a support vector regression (SVR) evaluation method based on improved grey wolf optimizer (IGWO) is proposed. Opposition-based learning strategy and cosine nonlinear convergence factor are introduced to improve the convergence performance and global optimization capability of GWO. IGWO algorithm is applied to the optimization of effectiveness evaluation parameters based on SVR. Based on the characteristics of RSS, the evaluation index system and effectiveness evaluation model are constructed. Finally, the validity of the model and the rationality of the method are verified by simulation evaluation of RSS under a certain operational scenario.