Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization

Muhammad Usman, Abubakr Awad, Wei Pang, George M. Coghill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inferring models of dynamic systems from their time series data is a challenging task for optimization algorithms due to its potentially expensive computational cost and underlying large search space. In this study, we aim to infer both the structure and parameters of a dynamic system model simultaneously by Particle Swarm Optimization (PSO), enhanced by effective stratified sampling strategies.
More specifically, we apply Latin Hyper Cube Sampling (LHS) with PSO. This leads to two novel swarm-inspired algorithms, LHS-PSO which can be used efficiently to learn the structure and parameters of simple and complex dynamic system models. We used a complex biological cancer model called Kinetochores, for assessing the performance of PSO and LHS-PSO. The experimental results
demonstrate that LHS-PSO can find promising solutions with corresponding structure and parameters, and it outperforms PSO during our experiments.
Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
EditorsManuel López-Ibáñez, Anne Auger, Thomas Stützle
Place of PublicationNew York, USA
PublisherACM
Pages101-102
Number of pages2
ISBN (Electronic)9781450367486
ISBN (Print)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
EventThe Genetic and Evolutionary Computation Conference GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Conference

ConferenceThe Genetic and Evolutionary Computation Conference GECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Dynamic Systems
  • Particle Swarm Optimization
  • Genetic Algorithm
  • Latin Hypercube Sampling
  • Learning Structure and Parameter
  • Parameter
  • Learning Structure

Fingerprint Dive into the research topics of 'Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization'. Together they form a unique fingerprint.

  • Cite this

    Usman, M., Awad, A., Pang, W., & Coghill, G. M. (2019). Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. In M. López-Ibáñez, A. Auger, & T. Stützle (Eds.), GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 101-102). ACM. https://doi.org/10.1145/3319619.3322010