Acceleration of solving the dynamic multi-objective network design problem using response surface methods

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

Abstract

Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorithms are needed and solving the lower level requires solving the dynamic user equilibrium problem. Using function approximation like response surface methods (RSM) in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three algorithms in which RSM are used in different ways in combination with the Strength Pareto Evolutionary Algorithm 2+ (SPEA2+) are compared with employing the SPEA2+ without the use of these methods. The results show that the algorithms using RSM methods accelerate the search considerably at the start, but tend to converge more quickly, possibly to a local optimum, and therefore loose their head start. Therefore, usage of function approximation is mainly of interest if a limited number of exact evaluations can be done or this can be used as a pre phase in a hybrid approach.
Original languageEnglish
Pages (from-to)17-29
Number of pages14
JournalJournal of intelligent transportation systems
Volume18
Issue number1
DOIs
Publication statusPublished - 2014

Fingerprint

Response Surface Method
Network Design
Evolutionary algorithms
Evolutionary Algorithms
Pareto
Accelerate
Externalities
Traffic Management
Approximation of Functions
Multi-objective Evolutionary Algorithm
Function Approximation
Equilibrium Problem
Hybrid Approach
Accessibility
Heuristics
Tend
Optimization Problem
Converge
Optimization
Evaluation

Keywords

  • IR-100531
  • METIS-278155

Cite this

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title = "Acceleration of solving the dynamic multi-objective network design problem using response surface methods",
abstract = "Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorithms are needed and solving the lower level requires solving the dynamic user equilibrium problem. Using function approximation like response surface methods (RSM) in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three algorithms in which RSM are used in different ways in combination with the Strength Pareto Evolutionary Algorithm 2+ (SPEA2+) are compared with employing the SPEA2+ without the use of these methods. The results show that the algorithms using RSM methods accelerate the search considerably at the start, but tend to converge more quickly, possibly to a local optimum, and therefore loose their head start. Therefore, usage of function approximation is mainly of interest if a limited number of exact evaluations can be done or this can be used as a pre phase in a hybrid approach.",
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author = "Wismans, {Luc Johannes Josephus} and {van Berkum}, {Eric C.} and M.C.J. Bliemer",
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Acceleration of solving the dynamic multi-objective network design problem using response surface methods. / Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.

In: Journal of intelligent transportation systems, Vol. 18, No. 1, 2014, p. 17-29.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Acceleration of solving the dynamic multi-objective network design problem using response surface methods

AU - Wismans, Luc Johannes Josephus

AU - van Berkum, Eric C.

AU - Bliemer, M.C.J.

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AB - Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorithms are needed and solving the lower level requires solving the dynamic user equilibrium problem. Using function approximation like response surface methods (RSM) in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three algorithms in which RSM are used in different ways in combination with the Strength Pareto Evolutionary Algorithm 2+ (SPEA2+) are compared with employing the SPEA2+ without the use of these methods. The results show that the algorithms using RSM methods accelerate the search considerably at the start, but tend to converge more quickly, possibly to a local optimum, and therefore loose their head start. Therefore, usage of function approximation is mainly of interest if a limited number of exact evaluations can be done or this can be used as a pre phase in a hybrid approach.

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