基于改进量子遗传算法的多层光学薄膜逆向设计OA

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.