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

机译:促进量子遗传算法在空气抗辐射混合组力部署中优化中的应用

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摘要

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.
机译:在本文中,提出了对空气抗辐射杂交组的抗电子干扰和防御空气的概念和力部署需求,在背景中分析了屏蔽角,部署距离和有效电子干扰等的关系基于杀灭区目标函数,建立了具有电子支持的空中RAID战斗,强制部署表面与空气防辐射混合组的优化模型。就杂交组力部署的特征而言,用自适应旋转角度改善了量子遗传算法(QGA),用改进的QGA解决了基于活物示例的问题。相比之下,改进的QGA在全局优化方面更好,收敛率和稳定性的比QGA,粒子群优化算法和量子涡流算法在表面到空气防辐射混合组力部署的优化问题中。

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