采用多准则决策分析的高功率微波源多目标优化设计

Multi-Objective Optimization Design of High-Power Microwave Source Based on Multi-Criteria Decision Making

为解决高功率微波源优化设计难度较大的问题,将多准则决策分析方法、遗传算法和全电磁粒子模拟软件UNIPIC相结合,提出了一种适于高功率微波源的多目标优化设计方法FSAWS-GA。通过对待优化器件的结构参数进行浮点编码生成种群个体,利用多准则决策分析方法中的主观目标和客观目标评价算法实现个体适应度评价,基于遗传算法实现种群进化,达到高功率微波源多目标优化设计的目的。对比测试结果表明:FSAWS-GA算法较传统单目标遗传优化算法,对高功率微波源优化有更好的适应性,能够优化出多性能指标均符合设计要求的器件结构。利用FSAWSGA对一种Ka波段的相对论返波管进行了结构优化测试,得到了输出功率为0.642GW、工作频率为26.68GHz、频率纯度为0.09、电子通过率为86.72%、能量转换效率为15.6%的新型器件结构。新型器件结构在确保多项性能指标均满足设计要求的前提下,输出功率较原器件提高了210.1%,验证了利用FSAWS-GA算法实现高功率微波源多目标优化设计的可行性。

To overcome the difficulty in optimization design of high-power microwave(HPM)sources,a multi-objective optimization design method of HPM sources,named FSAWS-GA,was proposed in this study based on multi-criteria decision making(MCDM),genetic algorithm,and fully electromagnetic particle simulation software UNIPIC.The structure parameters of the device to be optimized were used to generate individual chromosome by using float-encoding.The individual fitness evaluation was realized by using subjective-objective evaluation algorithms in MCDM method,and the population evolution was based on the genetic algorithm,thus realizing multi-objective optimization design of HPM sources.The comparative results showed that the FSAWS-GA was more suitable than the conventional single-objective genetic optimization algorithm for the multi-objective optimization design of HPM sources,and can be used to generate an optimized device with better comprehensive performance than the primary device.The FSAWS-GA was used to optimize a Ka-band relativistic back-wave oscillator(RBWO).An optimized RBWO was generated with output power of 0.642 GW,operating frequency of 26.68 GHz,frequency purity of 0.09,electron passing rate of 86.72%and beam-to-microwave conversion efficiency of 15.6%.The output power of the optimized RBWO was increased by 210.1%compared to the primary RBWO on the premise of ensuring that multiple performance indicators meet the requirements,which verifies the feasibility of using FSAWS-GA algorithm to achieve multi-objective optimization design of HPM sources.