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A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation

Title:

A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation

Lamotte, Raphaël A. F. (2014) A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation. Masters thesis, Concordia University.

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Abstract

The capacity of microscopic traffic simulation to estimate the environmental and road safety impacts opens the possibility to address the Network Design Problem from a new multi-objective point of view. Computation time, however, has hindered the use of this tool. The aim of this thesis was to find a continuous optimization method that would require only a very limited number of evaluations, and thus reduce the computation time. For this purpose, the most recent optimization literature was studied and two algorithms were selected: PAL and SMS-EGO. Both these algorithms rely on Gaussian process meta-models, but they are distinct with respect to the assumptions, criteria and methods used. They were then compared on a real-world case-study with NSGA-II, a genetic algorithm considered as state-of-the-art. Within the very limited computational budget allowed, SMS-EGO was found to outperform PAL and NSGA-II in the three configurations studied. However, the computational time required was still too important to allow for large scale optimization. To further accelerate the optimization process, three main adjustments were proposed, based on variable noise modeling, gradient-based optimization and conditional updates of the meta-models. Considering 20 runs for each optimization process, only variable noise modeling exhibited a statistically significant positive impact. The two other modifications also accelerated the optimization process on average, but high variability in the results led to p-values in the order of 0.15. Overall, the proposed optimization methodology represents a useful tool for transportation researchers to solve multi-objective optimization problems of limited scale.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Lamotte, Raphaël A. F.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Civil Engineering
Date:April 2014
Thesis Supervisor(s):Alecsandru, Ciprian
Keywords:Optimization; Network Design; Microscopic Simulation; Multi-objective; Gaussian processes; Meta-model
ID Code:978402
Deposited By: RAPHAEL ALI FRA LAMOTTE
Deposited On:16 Jun 2014 18:49
Last Modified:18 Jan 2018 17:46

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