Optimizing Equivalent Circuit Model Parameters of DFB Lasers With RSM Model and NSGA-II Algorithm

被引:4
|
作者
Ding, Qing-an [1 ,2 ]
Cheng, Xudong [2 ,3 ]
Liu, Huixin [2 ,3 ]
Wang, Xiaojuan [2 ,3 ]
Guo, Xiaohan [4 ]
Zheng, Li [2 ,3 ]
Li, Junkai [2 ,3 ]
Dai, Zhenfei [2 ,3 ]
Yang, Qunying [2 ,3 ]
Li, Jun [2 ,3 ]
机构
[1] Shandong Univ Sci & Technol, Sch Elect & Informat Engn, Qingdao 266510, Peoples R China
[2] Shandong Univ Sci & Technol, Microwave & Opt Commun Studio, Qingdao 266510, Peoples R China
[3] Shandong Univ Sci & Technol, Coll Elect Informat Engn, Qingdao 266510, Peoples R China
[4] Shandong Univ Qingdao, Sch Informat Sci & Engn, Qingdao 266237, Peoples R China
来源
IEEE PHOTONICS JOURNAL | 2022年 / 14卷 / 05期
基金
中国国家自然科学基金;
关键词
Integrated circuit modeling; Mathematical models; Optimization; Fiber lasers; Equivalent circuits; Genetic algorithms; Distributed feedback devices; DFB laser; parameter optimization; response surface methodology (RSM); non-dominated sorting genetic algorithm-II(NSGA-II); Pareto sorting; multi-objective optimization; BURIED HETEROSTRUCTURE LASER; DIRECT MODULATION BANDWIDTH; APPROXIMATE ANALYSIS; NONLINEAR OPERATION; GENETIC ALGORITHM; FEEDBACK; EXTRACTION; FREQUENCY; DYNAMICS;
D O I
10.1109/JPHOT.2022.3201109
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The parameters group of the distributed feedback (DFB) laser equivalent circuit model based on the single-mode rate equations is the key to precisely presenting DFB response characteristics, so a novel optimization solution based on the response surface methodology (RSM) is proposed to rapidly select the optimized parameters by the multi-objective algorithm. The RSM model is designed to match the DFB laser characteristics related to the direct-current and small-signal frequency response, and non-dominated sorting genetic algorithm-II (NSGA-II) attributes to elevating the RSM model optimizing to screen out an optimal set of parameters by Pareto sorting. To further verify the accuracy of the model, the resonant frequency (f(r)) and the threshold current (I-th) are considered the objective optimization variables to set the target values as 18 GHz and 11.5 mA. The single-objective and multi-objective optimization are analyzed and compared to each other, and the optimized results have shown good agreement with predicted values, such as lower I-th in the multi-objective optimization while close f(r) in both cases. It has been demonstrated that optimization makes it possible not only to exploit the potential of existing DFB lasers but also to provide guidance for the inverse design of laser.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Overview of NSGA-II for Optimizing Machining Process Parameters
    Yusoff, Yusliza
    Ngadiman, Mohd Salihin
    Zain, Azlan Mohd
    CEIS 2011, 2011, 15
  • [2] Parameter Optimization of Frazil Ice Evolution Model Based on NSGA-II Genetic Algorithm
    Chen, Yunfei
    Lian, Jijian
    Zhao, Xin
    Yang, Deming
    WATER, 2024, 16 (09)
  • [3] Multi-Objective Optimization of Laser Cladding Parameters Based on RSM and NSGA-II Algorithm
    Wang Yanyan
    Li Jiahao
    Shu Linsen
    Su Chengming
    LASER & OPTOELECTRONICS PROGRESS, 2022, 59 (07)
  • [4] Application of the NSGA-II Algorithm and Kriging Model to Optimise the Process Parameters for the Improvement of the Quality of Fresnel Lenses
    Chang, Hanjui
    Sun, Yue
    Wang, Rui
    Lu, Shuzhou
    POLYMERS, 2023, 15 (16)
  • [5] Online learning resources recommendation model based on improved NSGA-II algorithm
    Li, Hui
    Gong, Rongrong
    Hou, Pengfei
    Xing, Libao
    Jia, Dongbao
    Li, Haining
    ELECTRONIC RESEARCH ARCHIVE, 2023, 31 (05): : 3030 - 3049
  • [6] Estimation parameters of hydrocracking model with NSGA-ii (Non-dominated Sorting Genetic Algorithm) by using discrete kinetic lumping model
    Li, Guoqing
    Cai, Chuxuan
    FUEL, 2017, 200 : 333 - 344
  • [7] Optimizing Wall Insulation Material Parameters in Renovation Projects using NSGA-II
    Uyduran, Hizir Gokhan
    Iseri, Orcun Koral
    Ustunes, Yarkin
    Dursun, Onur
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 4208 - 4213
  • [8] Reverse reconstruction of motorcycle-car accident based on response surface model and NSGA-II algorithm
    Wang, Qian
    Lou, Yunfeng
    Li, Tong
    Jin, Xianlong
    Kong, Lingshuang
    Hou, Xinyi
    INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2022, 27 (01) : 107 - 116
  • [9] Multi-objective optimization research of printed circuit heat exchanger based on RSM and NSGA-II
    Lv, Junshuai
    Sun, Yuwei
    Lin, Jie
    Luo, Xinyu
    Li, Peiyue
    APPLIED THERMAL ENGINEERING, 2024, 254
  • [10] Model calibration and exergoeconomic optimization with NSGA-II applied to a residential cogeneration
    Martinez, Sandra
    Perez, Estibaliz
    Eguia, Pablo
    Erkoreka, Aitor
    Granada, Enrique
    APPLIED THERMAL ENGINEERING, 2020, 169 (169)