Road Traffic System: Optimization Using Ant Systems
Let us look at these social insects, which are relatively simple however, they can perform effective strategy following the simple, adaptive to local rules, which allows them to change according to the environment for survival. This uniqueness of the insect world helps us to understand that complex...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2003
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| Online Access: | https://eprints.nottingham.ac.uk/24533/ |
| _version_ | 1848792804223877120 |
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| author | Yeong, Kim Ming |
| author_facet | Yeong, Kim Ming |
| author_sort | Yeong, Kim Ming |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Let us look at these social insects, which are relatively simple however, they can perform effective strategy following the simple, adaptive to local rules, which allows them to change according to the environment for survival. This uniqueness of the insect world helps us to understand that complex situation does have solution. The study of ant colonies behavior is an interesting issue that provides good modeling solution for difficult optimization and distributed control problems. Transportation and vehicle routing are of great importance. Road traffic congestion has become a major concern for many cities. The design of cities’ infrastructures for automobiles requires great challenges. The increase of traffic volumes reflects the distribution costs of the product’s cost and standard of living of the cities. Having a good and efficient road traffic system will benefit the society as a whole. This thesis introduces the Road Traffic System Optimization using Ant System algorithms for solving road traffic congestion. The Road Traffic System is an adaptive, distributed, car-agent based algorithms, which inspired by recent work on the ant colony metaphor. The Road Traffic System showed both good performance and robustness under all the experimental conditions. |
| first_indexed | 2025-11-14T18:50:13Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-24533 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:50:13Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-245332018-02-01T03:40:19Z https://eprints.nottingham.ac.uk/24533/ Road Traffic System: Optimization Using Ant Systems Yeong, Kim Ming Let us look at these social insects, which are relatively simple however, they can perform effective strategy following the simple, adaptive to local rules, which allows them to change according to the environment for survival. This uniqueness of the insect world helps us to understand that complex situation does have solution. The study of ant colonies behavior is an interesting issue that provides good modeling solution for difficult optimization and distributed control problems. Transportation and vehicle routing are of great importance. Road traffic congestion has become a major concern for many cities. The design of cities’ infrastructures for automobiles requires great challenges. The increase of traffic volumes reflects the distribution costs of the product’s cost and standard of living of the cities. Having a good and efficient road traffic system will benefit the society as a whole. This thesis introduces the Road Traffic System Optimization using Ant System algorithms for solving road traffic congestion. The Road Traffic System is an adaptive, distributed, car-agent based algorithms, which inspired by recent work on the ant colony metaphor. The Road Traffic System showed both good performance and robustness under all the experimental conditions. 2003 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/24533/1/yeongkimming.pdf Yeong, Kim Ming (2003) Road Traffic System: Optimization Using Ant Systems. [Dissertation (University of Nottingham only)] (Unpublished) |
| spellingShingle | Yeong, Kim Ming Road Traffic System: Optimization Using Ant Systems |
| title | Road Traffic System: Optimization Using Ant Systems |
| title_full | Road Traffic System: Optimization Using Ant Systems |
| title_fullStr | Road Traffic System: Optimization Using Ant Systems |
| title_full_unstemmed | Road Traffic System: Optimization Using Ant Systems |
| title_short | Road Traffic System: Optimization Using Ant Systems |
| title_sort | road traffic system: optimization using ant systems |
| url | https://eprints.nottingham.ac.uk/24533/ |