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Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 1 (2018)

DOI
10.11591/ijece.v8i1.pp246-253
ISSN
2088-8708, 2088-8708
Published
2018-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.

Collaboration network of the authors

6 authors · 9 co-authorship links · 2 across different organisations

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  • University of Mataram
  • Kumamoto University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Asno Azzawagaam FirdausAffiliation unknown1
Aditya Perwira Joan DwitamaUniversity of Mataram1
Mustiari MustiariUniversity of Mataram1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm</dc:title><dc:creator>Wijaya, I Gede Pasek Suta</dc:creator><dc:creator>Uchimura, Keeichi</dc:creator><dc:creator>Koutaki, Gou</dc:creator><dc:description>&lt;jats:p&gt;A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2018-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v8i1.pp246-253</dc:identifier><dc:identifier>10.11591/ijece.v8i1.pp246-253</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 8 No 1 (2018); 246</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/10119/8778</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>