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

被引:5
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
[41]   BS-CDE: An Optimal Charging Strategy Model of BSSs for BSHTs Based on Improved NSGA-II Algorithm [J].
Huang, Yulong ;
Niu, Naiping ;
Chen, Zehua ;
Liu, Xiaofeng .
PROCESSES, 2025, 13 (03)
[42]   Optimization of Operation Strategies for an Interbasin Water Diversion System Using an Aggregation Model and Improved NSGA-II Algorithm [J].
Xu, Wei ;
Chen, Cong .
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2020, 146 (05)
[43]   Presenting a Management Model for a Multiobjective Sustainable Supply Chain in the Cellulosic Industry and Its Implementation by the NSGA-II Meta-Heuristic Algorithm [J].
Mehri Charvadeh, Meisam ;
Pourmousa, Shademan ;
Tajdini, Ajang ;
Tamjidi, Abbas ;
Safdari, Vahidreza .
DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2022, 2022
[44]   An optimized fuzzy deep learning model for data classification based on NSGA-II [J].
Yazdinejad, Abbas ;
Dehghantanha, Ali ;
Parizi, Reza M. ;
Epiphaniou, Gregory .
NEUROCOMPUTING, 2023, 522 :116-128
[45]   Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II [J].
Fu, Jun ;
Yuan, Haikuo ;
Zhang, Depeng ;
Chen, Zhi ;
Ren, Luquan .
APPLIED SCIENCES-BASEL, 2020, 10 (05)
[46]   Modeling and optimizing linear projects using LSM and Non-dominated Sorting Genetic Algorithm (NSGA-II) [J].
Altanany, M. Yasser ;
Badawy, Mohamed ;
Ebrahim, Gamal A. ;
Ehab, A. .
AUTOMATION IN CONSTRUCTION, 2024, 165
[47]   Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model [J].
Jiang, Rongchao ;
Jin, Zhenchao ;
Liu, Dawei ;
Wang, Dengfeng .
MACHINES, 2021, 9 (06)
[48]   Multi-objective optimization for greenhouse light environment using Gaussian mixture model and an improved NSGA-II algorithm [J].
Liu, Tan ;
Yuan, Qingyun ;
Ding, Xiaoming ;
Wang, Yonggang ;
Zhang, Dapeng .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 205
[49]   Optimal Selection of Seed-Trees Using the Multi-Objective NSGA-II Algorithm and a Seed Dispersal Model [J].
Nanos, Nikos ;
Garcia-del-Rey, Eduardo ;
Gil, Luis .
FORESTS, 2024, 15 (03)
[50]   A quantitative model and solution of reaction heat for microfluidic chips based on Kriging and NSGA-II [J].
Gan, Yi ;
Yang, Minghao ;
Sun, FuJia ;
Yang, Lihong .
THERMOCHIMICA ACTA, 2020, 694