Application of Improved Quantum Genetic Algorithm in Optimization for Surface to Air Anti-Radiation Hybrid Group Force Deployment

一种改进量子遗传算法在地空反辐射混编群兵力配置优化中的应用

¹ Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China
² Rocket Force University of Engineering Xi'an 710025, China

Received: 9 October 2018

Abstract

In this paper, the concept and force deployment needs for anti-electronic jamming and defensing air of surface to air anti-radiation hybrid group was presented, the relationship between shield angle, deployment distance and effective electronic interference etc, were analyzed in the background of air raid battle which is with electronic support, force deployment optimization model of surface to air anti-radiation hybrid group was built based on the kill zone target function. In terms of the characteristic of hybrid group force deployment, quantum genetic algorithm (QGA) was improved with self-adaption rotation angle, the problem which was based on a living example was solved with improved QGA. By contrast, the improved QGA is better in the respects of global optimization, rate of convergence and stability than QGA, particle swarm optimization algorithm and quantum vortex algorithm in the problem of optimization for surface to air anti-radiation hybrid group force deployment.

摘要

提出了地空反辐射混编群概念及防电磁干扰与抗空中进袭的综合兵力配置需求，基于电子支援下的空袭作战背景，分析了掩护角、配置距离、有效干扰等要素的相互关系，并以杀伤区面积为目标函数，构建了地空反辐射混编群兵力配置优化模型。结合混编群兵力配置特点，对量子遗传算法进行了旋转角的自适应改进，在设置想定实例的基础上，运用改进的量子遗传算法对问题进行求解，并与传统量子遗传算法、粒子群算法和量子涡流算法计算结果进行了对比分析。结果表明：运用改进量子遗传算法求解地空反辐射混编群兵力配置优化问题，全局寻优能力更强、收敛速度更快、稳定性更好。