摘要
为了解决空军航材配置中存在的多目标决策问题,提高航材配置优化决策的精度和效率,建立了航材配置的多目标优化数学模型,并将帕累托支配相关概念与一种先进的群体智能算法--蚁狮算法相结合,构建了多目标蚁狮算法求解航材配置优化模型的思路和框架。通过数值仿真,得到了最优配置方案集合,并进一验证了该算法的效果和效率。研究表明,上述算法能够很好的解决航材配置中的多目标优化问题,为空军航材保障提供了一种有效的优化方案。
In order to solve the problem of multi-objective decision making and improve the precision and efficiency of the optimization decision of aviation parts allocation, a mathematical model of multi-objective optimization of aviation parts allocation was established. In this paper, the concept of Pareto domination was combined with an advanced swarm intelligence algorithm- the ant lion algorithm, to construct a multi-objective ant lion algorithm to solve the optimization model of air material configuration. A set of optimal solution was obtained through numerical simulation, and the effectiveness and efficiency of the algorithm were proved. The research shows that the algorithm can solve the multi-objective optimization problem in the configuration of air parts, which provides an effective optimization scheme for air force air parts support.
作者
张家维
李昊
ZHANG Jia-wei;LI Hao(Department of Aviation Four Stations,Air Force Logistics College,Xuzhou Jiangsu 221000,China)
出处
《计算机仿真》
北大核心
2019年第7期71-74,115,共5页
Computer Simulation
关键词
航材配置
多目标优化
蚁狮算法
Aviation parts allocation
Multi-objective optimization
Ant lion optimizer
