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Sökning: WFRF:(Kuzmanovski Vladimir)

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1.
  • Agriesti, Serio, et al. (författare)
  • A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models
  • 2023
  • Ingår i: IEEE Open Journal of Intelligent Transportation Systems. - 2687-7813. ; 4, s. 740-754
  • Tidskriftsartikel (refereegranskat)abstract
    • Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors' knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.
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2.
  • Kuzmanovski, Vladimir, et al. (författare)
  • Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models
  • 2021
  • Ingår i: Advances in Intelligent Data Analysis XIX. - Cham : Springer. - 9783030742515 ; , s. 171-183
  • Konferensbidrag (refereegranskat)abstract
    • Activity-based transportation models simulate demand and supply as a complex system and therefore large set of parameters need to be adjusted. One such model is Preday activity-based model that requires adjusting a large set of parameters for its calibration on new urban environments. Hence, the calibration process is time demanding, and due to costly simulations, various optimisation methods with dimensionality reduction and stochastic approximation are adopted. This study adopts Bayesian Optimisation for Likelihood-free Inference (BOLFI) method for calibrating the Preday activity-based model to a new urban area. Unlike the traditional variant of the method that uses Gaussian Process as a surrogate model for approximating the likelihood function through modelling discrepancy, we apply a composite surrogate model that encompasses Random Forest surrogate model for modelling the discrepancy and Gaussian Mixture Model for estimating the its density. The results show that the proposed method benefits the extension and improves the general applicability to high-dimensional settings without losing the efficiency of the Bayesian Optimisation in sampling new samples towards the global optima.
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3.
  • Kuzmanovski, Vladimir, et al. (författare)
  • Semi-parametric approach to random forests for high-dimensional Bayesian optimisation
  • 2022
  • Ingår i: Discovery Science. - Cham : Springer. - 9783031188398 - 9783031188404 ; , s. 418-428
  • Konferensbidrag (refereegranskat)abstract
    • Calibration of simulation models and hyperparameter optimisation of machine learning and deep learning methods are computationally demanding optimisation problems, for which many state-of-the-art optimisation methods are adopted and applied in various studies. However, their performances come to a test when the parameter optimisation problems exhibit high-dimensional spaces and expensive evaluation of models’ or methods’ settings. Population-based (evolutionary) methods work well for the former but not suitable for expensive evaluation functions. On the opposite, Bayesian optimisation eliminates the necessity of frequent simulations to find the global optima. However, the computational demand rises significantly as the number of parameters increases. Bayesian optimisation with random forests has overcome issues of its state-of-the-art counterparts. Still, due to the non-parametric output, it fails to utilise the capabilities of available acquisition functions. We propose a semi-parametric approach to overcome such limitations to random forests by identifying a mixture of parametric components in their outcomes. The proposed approach is evaluated empirically on four optimisation benchmark functions with varying dimensionality, confirming the improvement in guiding the search process. Finally, in terms of running time, it scales linearly with respect to the dimensionality of the search space.
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