Multi-objective Optimization of Traffic Externalities using Tolls: A Comparison of Genetic Algorithm with Game Theoretical Approach.

Anthony Ohazulike, Ties Brands

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multi-objective problem and compare the results with those of the well-known genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.
Original languageEnglish
Title of host publicationProceedings of IEEE Conference on Evolutionary Computation, Cancún, Mexico, June 20-23, 2013
Place of PublicationCancún
PublisherIEEE
Pages2465-2472
Number of pages8
ISBN (Print)978-1-4799-0452-5
DOIs
Publication statusPublished - 20 Jun 2013
Event16th IEEE Conference on Evolutionary Computation 2013 - Cancun, Mexico
Duration: 20 Jun 201323 Jun 2013

Publication series

Name
PublisherIEEE

Conference

Conference16th IEEE Conference on Evolutionary Computation 2013
CountryMexico
CityCancun
Period20/06/1323/06/13

Keywords

  • METIS-296729
  • IR-86801

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