目的 为实现光谱选择性薄膜材料的高效逆向设计,提出了一种改进的量子遗传算法（Improved Quantum Genetic Algorithm, iQGA）,用于多层光学薄膜结构的智能优化。方法 算法在传统量子遗传算法的基础上引入离散—连续混合编码方式与多精英引导机制,使材料类型与厚度参数在统一框架下实现协同优化,从而提高算法全局搜索能力与收敛效率。正向设计通过传输矩阵法对候选结构进行光学响应计算,并采用类交叉熵损失函数作为适应度评估指标,引入局部掩码机制以实现对目标光谱特性的全局精确拟合。结果 对比表明,iQGA在收敛速度、稳定性及解质量方面均优于灰狼优化算法、遗传算法和传统量子遗传算法。进一步将iQGA应用于透明辐射冷却、热光伏、红外隐身及窄带发射等四种典型设计任务,所得多层膜结构能达到预期光谱响应要求,全局平均绝对误差MAE分别为0.107、0.077、0.188和0.166,验证了方法的高效性与可行性。结论 iQGA为光谱选择性材料的逆向设计提供了一种高效且可扩展的优化途径,若与其他数值仿真方法结合或利用神经网络模型扩展,该方法有望推广至超表面及复杂纳米光学结构的通用逆向设计中。
Abstract
Spectrally selective multilayer thin films play a critical role in a wide range of photonic and energy-related applications, including transparent radiative cooling, thermophotovoltaic energy conversion, infrared stealth, and narrowband thermal emission. Designing such multilayer optical structures is inherently challenging due to the large combinatorial search space arising from discrete material selection, continuous thickness variation, and variable layer numbers. Conventional forward design approaches based on empirical rules or manual parameter tuning are often inefficient and insufficient for meeting complex, application-specific spectral requirements. To address these challenges, the work aims to present an improved quantum genetic algorithm (iQGA) as an efficient inverse design framework for multilayer optical thin films. The proposed iQGA is developed based on the conventional quantum genetic algorithm and introduces a discrete-continuous hybrid encoding scheme, enabling the simultaneous optimization of material types (discrete variables) and layer thicknesses (continuous variables) within a unified framework. In addition, the conventional evolutionary update strategy is replaced by a multi-elite guidance mechanism, in which the update of each quantum individual is jointly guided by the global historical optimum, the current population optimum, and the individual's personal best solution. This strategy enhances global exploration while preserving elite solutions, thereby improving convergence behavior and robustness against premature stagnation. For forward evaluation, the optical responses of candidate multilayer structures are computed with the transfer matrix method, which provides fast and accurate solutions for multilayer structures over a wide spectral range from the visible to the infrared. The discrepancy between the optimized solution and the target spectrum is quantified through a modified cross-entropy-like loss function, which strongly penalizes large deviations while tolerating small local errors. Combined with a masking mechanism that selectively ignores unconstrained spectral bands or optical channels, the algorithm enables inverse design of functional thin-film materials satisfying wide-band spectral matching requirements. The performance of the proposed iQGA is benchmarked against the grey wolf optimizer, the classical genetic algorithm, and the standard quantum genetic algorithm under identical search spaces and similar hyperparameter settings. Performance verification results demonstrate that the iQGA consistently achieves faster convergence, higher solution quality, and improved stability across repeated runs, highlighting the effectiveness of the hybrid encoding and multi-elite guidance strategy. The iQGA is further applied to four representative inverse design scenarios: transparent radiative cooling, thermophotovoltaic emitters, infrared stealth materials, and narrowband emitters. In all cases, the optimized multilayer thin-film structures exhibit spectral responses highly consistent with the prescribed targets and their global mean absolute errors (MAE) are 0.107, 0.077, 0.188, and 0.166, respectively. These results confirm that the proposed method is not limited to a specific application or spectral band, but instead provides a unified optimization framework for diverse spectrally selective multilayer thin-film systems. Overall, the iQGA offers a flexible and extensible solution for the inverse design of multilayer optical materials. By decoupling the forward solver from the optimization algorithm, the proposed framework can be readily combined with alternative numerical methods or neural-network-based surrogate models, enabling future extension to meta-surfaces and more complex photonic structures.
关键词
多层薄膜设计 /
光谱选择性 /
优化算法 /
量子遗传算法 /
传输矩阵法
Key words
multilayer thin film design /
spectrally selective /
optimization algorithm /
quantum genetic algorithm /
transfer matrix method
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基金
国家自然科学基金（52466013）;江西省大学生创新训练计划（S202511318066）
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