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 publicationGenetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic
Place of PublicationNew York, USA
PublisherACM
Number of pages2
ISBN (Print)978-1-4503-6748-6
DOIs
Publication statusAccepted/In press - 21 Mar 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

Fingerprint

Particle swarm optimization (PSO)
Dynamical systems
Sampling
Time series
Costs
Experiments

Keywords

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

Cite this

Usman, M., Awad, A., Pang, W., & Coghill, G. M. (Accepted/In press). Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. In Genetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic New York, USA: ACM. https://doi.org/10.1145/3319619.3322010

Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. / Usman, Muhammad; Awad, Abubakr; Pang, Wei; Coghill, George M.

Genetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic. New York, USA : ACM, 2019.

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

Usman, M, Awad, A, Pang, W & Coghill, GM 2019, Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. in Genetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, USA, The Genetic and Evolutionary Computation Conference GECCO 2019, Prague, Czech Republic, 13/07/19. https://doi.org/10.1145/3319619.3322010
Usman M, Awad A, Pang W, Coghill GM. Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. In Genetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic. New York, USA: ACM. 2019 https://doi.org/10.1145/3319619.3322010
Usman, Muhammad ; Awad, Abubakr ; Pang, Wei ; Coghill, George M. / Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. Genetic and Evolutionary Computation Conference Companion (GECCO ’19 Companion), July 13–17, 2019, Prague, Czech Republic. New York, USA : ACM, 2019.
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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 resultsdemonstrate that LHS-PSO can find promising solutions with corresponding structure and parameters, and it outperforms PSO during our experiments.",
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