The research on nanophotonic devices has made great progress during the past decades. It is the unremitting pursuit of researchers that realize various device functions to meet practical applications. However, most of the traditional methods rely on human experience and physical inspiration for structural design and parameter optimization, which usually require a lot of resources, and the performance of the designed device is limited. Intelligent algorithms, which are composed of rich optimized algorithms, show a vigorous development trend in the field of nanophotonic devices in recent years. The design of nanophotonic devices by intelligent algorithms can break the restrictions of traditional methods and predict novel configurations, which is universal and efficient for different materials, different structures, different modes, different wavelengths, etc. In this review, intelligent algorithms for designing nanophotonic devices are introduced from their concepts to their applications, including deep learning methods, the gradient-based inverse design method, swarm intelligence algorithms, individual inspired algorithms, and some other algorithms. The design principle based on intelligent algorithms and the design of typical new nanophotonic devices are reviewed. Intelligent algorithms can play an important role in designing complex functions and improving the performances of nanophotonic devices, which provide new avenues for the realization of photonic chips.

针对红外强度图和红外偏振度图融合问题，提出了基于烟花算法优化空域加权平均法的智能图像融合方法。在构建优化问题模型的基础上，确定了烟花算法的边界条件。通过引入相对熵权值建立了基于综合相对熵的适应度函数。最后，与6种典型的传统融合方法在“ground”、“truck”、“car”3组红外强度和偏振度图像数据上进行了融合实验，对融合结果进行了客观评价和视觉效果评价。实验结果表明：所提方法可以有效实现红外强度图和红外偏振度图的融合，较好保留了红外强度和红外偏振特征。综合视觉效果和客观评价结果，在相对熵、总结构相似性、总互信息指标上优于比较算法。