Uncovering personal circadian responses to light through particle swarm optimization

被引:0
|
作者
Vicente-Martinez, Jesus [1 ,2 ]
Bonmati-Carrion, Maria Angeles [1 ,2 ]
Madrid, Juan Antonio [1 ,2 ]
Rol, Maria Angeles [1 ,2 ]
机构
[1] Univ Murcia, Dept Physiol, Coll Biol, Chronobiol Lab,Mare Nostrum Campus,IUIE,IMIB Arrix, Murcia 30100, Spain
[2] Inst Salud Carlos III, Ciber Fragilidad & Envejecimiento Saludable, Madrid 28029, Spain
关键词
Particle swarm optimization; Kronauer ' s oscillator model; Circadian personalization; Circadian response to light; Parameter optimization of ordinary differential; equations; Heuristic algorithms; CYCLE OSCILLATOR MODEL; PARAMETER-ESTIMATION; PHASE; PACEMAKER; DYNAMICS; EXPOSURE; PERIOD;
D O I
10.1016/j.cmpb.2023.107933
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Background and objectives: Kronauer's oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider interindividual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer's oscillator model.Methods: A Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model. Results: We demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user.Conclusions: The robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Estimation of Layered Structures Using Backscattering Responses and Particle Swarm Optimization
    Nakamura, Koki
    Ono, Yutaro
    Shibata, Tsugumichi
    Asia-Pacific Microwave Conference Proceedings, APMC, 2022, 2022-November : 842 - 844
  • [42] Estimation of Layered Structures Using Backscattering Responses and Particle Swarm Optimization
    Nakamura, Koki
    Ono, Yutaro
    Shibata, Tsugumichi
    2022 ASIA-PACIFIC MICROWAVE CONFERENCE (APMC), 2022, : 842 - 844
  • [43] Improvement of Particle Swarm Optimization Focusing on Diversity of the Particle Swarm
    Hayashida, Tomohiro
    Nishizaki, Ichiro
    Sekizaki, Shinya
    Takamori, Yuki
    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 191 - 197
  • [44] Optimization and Simulation of Green Light Duration at Intersection with Particle Swarm Optimization and Cellular Automata
    Hamami, Faqih
    Akbar, Saiful
    2018 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY SYSTEMS AND INNOVATION (ICITSI), 2018, : 110 - 114
  • [45] Optimization of Plant Light Source Based on Simulated Annealing Particle Swarm Optimization Algorithm
    Cui, Shigang
    Lv, Huimin
    Wu, Xingli
    Zhang, Yongli
    He, Lin
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 700 - 703
  • [46] Topology Optimization of Particle Swarm Optimization
    Li, Fenglin
    Guo, Jian
    ADVANCES IN SWARM INTELLIGENCE, PT1, 2014, 8794 : 142 - 149
  • [47] Topology optimization of particle swarm optimization
    1600, Springer Verlag (8794):
  • [48] Resemblance of Biological Particle Swarm Optimization and Particle Swarm Optimization for CBFR by using NN
    Dubey, Deepika
    Tomar, Geetam Singh
    MATERIALS TODAY-PROCEEDINGS, 2020, 29 : 408 - 419
  • [49] Gaussian-Distributed Particle Swarm Optimization: A Novel Gaussian Particle Swarm Optimization
    Lee, Joon-Woo
    Lee, Ju-Jang
    2013 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2013, : 1122 - 1127
  • [50] Asynchronous Particle Swarm Optimization for Swarm Robotics
    Ab Aziz, Nor Azlina
    Ibrahim, Zuwairie
    INTERNATIONAL SYMPOSIUM ON ROBOTICS AND INTELLIGENT SENSORS 2012 (IRIS 2012), 2012, 41 : 951 - 957