基于改进混合量子遗传算法的二相编码雷达波形优化

Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm

针对当前雷达波形优化算法搜索策略单一、适用范围受限的问题,提出了一种基于改进混合量子遗传算法的二相编码雷达波形优化算法。所提算法采用了一种新的自适应旋转角度策略,根据迭代次数和余弦相似度动态调整旋转角度,提高了算法的收敛速度、全局搜索能力和求解质量。仿真结果表明,与遗传算法、基本量子遗传算法和混合量子遗传算法相比,对于包含了单峰、多峰和非凸优化问题的6个标准测试函数,所提算法在质量和资源消耗上均表现更好;对于二相编码雷达波形优化,证实了使用所提算法优化波形是可行和有效的。

To overcome the problem of single search focus and limited application scope of existing algorithm,a binary phase-coded radar waveform optimization(RWO)based on improved hybrid quantum genetic algorithm(IHQGA)was proposed.IHQGA adopted a novel self-adaptive rotation angle strategy,which dynamically adjusted the rotation angle based on evolutionary process and cosine similarity.The convergence speed,global search capability,and solution quality were improved.Simulation results demonstrate that compared with genetic algorithms,basic quantum genetic algorithms,and hybrid quantum genetic algorithms,IHQGA performs better in terms of solution quality and resource consumption for six benchmark functions that include single-peak,multi-peak,and non-convex optimization problems.Additionally,for binary phase-coded RWO,which verifies the feasibility and effectiveness of IHQGA in WO.