基于IMBFO算法的干扰资源优化  

Optimization of Interference Resources Based on IMBFO Algorithm

作　　者：吕锐 吴达[1] 杨宇 张泽 郜阳 LYU Rui;WU Da;YANG Yu;ZHANG Ze;GAO Yang(Air and Missile Defense College,Air Force Engineering University,Xi'an 710000,China)

机构地区：[1]空军工程大学防空反导学院,西安710000

出　　处：《电光与控制》2021年第9期15-19,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61703424)。

摘　　要：传统的战场决策多依赖于人工经验进行实施,随着实际战争电磁环境的复杂性和恶劣性逐渐增长,传统决策方法略显不足。针对此问题,将干扰资源的优化决策问题建模为最小化干扰功率、最小化系统效能匹配度、最大化压制概率的多目标函数约束模型,通过对传统多目标细菌觅食优化(MBFO)算法进行改进,利用改进多目标细菌觅食优化(IMBFO)算法对问题进行求解,使得决策方案更加科学、合理。仿真结果表明,IMBFO算法能够有效求解该问题,并与MBFO算法、带有精英保留策略的快速非支配多目标优化算法(NSGA-II)相比,具有良好的分布性和收敛性。Traditional battlefield decision-making mostly relies on artificial experiences for implementation.Since the electromagnetic environment of actual warfare is becoming more and more complex and toughthe traditional decision-making methods are slightly insufficient. Aiming at the problem,the optimal decisionmaking problem of interference resources is modeled as a multi-objective constrained function,with objectives of minimum interference power,minimum system performance matching degree,and maximum suppression probability. Improvement is made to the traditional Multi-objective Bacterial Foraging Optimization( MBFO) algorithm,and the Improved MBFO( IMBFO) algorithm is used to solve the problem,which makes the decision plan more scientific and more reasonable. The simulation results show that: 1) The IMBFO works effectively;and 2) Compared with MBFO and Non-dominated Sorting Genetic Algorithm-II( NSGA-II) algorithms,it has fine distribution and convergence performance.

关 键 词：电子干扰 干扰决策 多目标函数 IMBFO NSGA-II 

分 类 号：TN972[电子电信—信号与信息处理]