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Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses

This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and mu...

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Main Authors: Tarajo, Buba Ahmed, Lee, Lai Soon
Format: Article
Language:English
Published: Hindawi Limited 2019
Online Access:http://psasir.upm.edu.my/id/eprint/80106/1/Hybrid%20Differential%20Evolution-Particle%20Swarm%20Optimization%20Algorithm%20for%20Multiobjective%20Urban%20Transit%20Network%20Design%20Problem%20with%20Homogeneous%20Buses.pdf
http://psasir.upm.edu.my/id/eprint/80106/
https://www.hindawi.com/journals/mpe/2019/5963240/
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spelling my.upm.eprints.801062020-09-22T01:49:51Z http://psasir.upm.edu.my/id/eprint/80106/ Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses Tarajo, Buba Ahmed Lee, Lai Soon This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives. Hindawi Limited 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80106/1/Hybrid%20Differential%20Evolution-Particle%20Swarm%20Optimization%20Algorithm%20for%20Multiobjective%20Urban%20Transit%20Network%20Design%20Problem%20with%20Homogeneous%20Buses.pdf Tarajo, Buba Ahmed and Lee, Lai Soon (2019) Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses. Mathematical Problems in Engineering, 2019. art. no. 963240,. pp. 1-16. ISSN 1024-123X; ESSN: 1563-5147 https://www.hindawi.com/journals/mpe/2019/5963240/ 10.1155/2019/5963240
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.
format Article
author Tarajo, Buba Ahmed
Lee, Lai Soon
spellingShingle Tarajo, Buba Ahmed
Lee, Lai Soon
Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
author_facet Tarajo, Buba Ahmed
Lee, Lai Soon
author_sort Tarajo, Buba Ahmed
title Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
title_short Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
title_full Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
title_fullStr Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
title_full_unstemmed Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
title_sort hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses
publisher Hindawi Limited
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/80106/1/Hybrid%20Differential%20Evolution-Particle%20Swarm%20Optimization%20Algorithm%20for%20Multiobjective%20Urban%20Transit%20Network%20Design%20Problem%20with%20Homogeneous%20Buses.pdf
http://psasir.upm.edu.my/id/eprint/80106/
https://www.hindawi.com/journals/mpe/2019/5963240/
_version_ 1680322360367906816
score 13.211869

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