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Ant Colony Optimization approaches for the Sequential Ordering Problem
We present two algorithms within the framework of the Ant Colony Optimization (ACO) metaheuristic. The rst algorithm seeks to increase the exploration bias of Gambardella et al.'s (2012) Enhanced Ant Colony System (EACS) model, a model which heavily increases the exploitation bias of the already hi...
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2014
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| _version_ | 1867613416466677760 |
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| access_status_str | Open Access |
| author | Ezzar, Ahmed Mohamed Alaa El-din |
| author_browse | Ezzar, Ahmed Mohamed Alaa El-din |
| author_facet | Ezzar, Ahmed Mohamed Alaa El-din |
| author_sort | Ezzar, Ahmed Mohamed Alaa El-din |
| collection | Thesis |
| dc_rights_str_mv | The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. |
| description | We present two algorithms within the framework of the Ant Colony Optimization (ACO) metaheuristic. The rst algorithm seeks to increase the exploration bias of Gambardella et al.'s (2012) Enhanced Ant Colony System (EACS) model, a model which heavily increases the exploitation bias of the already highly exploitative ACS model in order to gain the bene t of increased speed. Our algorithm aims to strike a balance between these two models. The second is also an extension of EACS, based on Jayadeva et al.'s (2013) EigenAnt algorithm. EigenAnt aims to avoid the problem of stagnation found in ACO algorithms by, among other unique properties, utilizing a selective rather than global pheromone evaporation model, and by discarding heuristics in the solution construction phase. A performance comparison between our two models, the legacy ACS model, and the EACS model is presented. The Sequential Ordering Problem (SOP), one of the main problems used to demonstrate EACS, and one still actively studied to this day, was utilized to conduct the comparison. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2204 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:47.730Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2204 Ant Colony Optimization approaches for the Sequential Ordering Problem Ezzar, Ahmed Mohamed Alaa El-din We present two algorithms within the framework of the Ant Colony Optimization (ACO) metaheuristic. The rst algorithm seeks to increase the exploration bias of Gambardella et al.'s (2012) Enhanced Ant Colony System (EACS) model, a model which heavily increases the exploitation bias of the already highly exploitative ACS model in order to gain the bene t of increased speed. Our algorithm aims to strike a balance between these two models. The second is also an extension of EACS, based on Jayadeva et al.'s (2013) EigenAnt algorithm. EigenAnt aims to avoid the problem of stagnation found in ACO algorithms by, among other unique properties, utilizing a selective rather than global pheromone evaporation model, and by discarding heuristics in the solution construction phase. A performance comparison between our two models, the legacy ACS model, and the EACS model is presented. The Sequential Ordering Problem (SOP), one of the main problems used to demonstrate EACS, and one still actively studied to this day, was utilized to conduct the comparison. 2014-02-01T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1205 https://fount.aucegypt.edu/context/etds/article/2204/viewcontent/Ant_20Colony_20Optimization_20Approaches_20for_20the_20Sequential_20Ordering_20Problem.pdf The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. Theses and Dissertations AUC Knowledge Fountain Combitorial optimization Statistical hypothesis testing |
| spellingShingle | Combitorial optimization Statistical hypothesis testing Ezzar, Ahmed Mohamed Alaa El-din Ant Colony Optimization approaches for the Sequential Ordering Problem |
| title | Ant Colony Optimization approaches for the
Sequential Ordering Problem |
| title_full | Ant Colony Optimization approaches for the
Sequential Ordering Problem |
| title_fullStr | Ant Colony Optimization approaches for the
Sequential Ordering Problem |
| title_full_unstemmed | Ant Colony Optimization approaches for the
Sequential Ordering Problem |
| title_short | Ant Colony Optimization approaches for the
Sequential Ordering Problem |
| title_sort | ant colony optimization approaches for the sequential ordering problem |
| topic | Combitorial optimization Statistical hypothesis testing |
| url | https://fount.aucegypt.edu/etds/1205 https://fount.aucegypt.edu/context/etds/article/2204/viewcontent/Ant_20Colony_20Optimization_20Approaches_20for_20the_20Sequential_20Ordering_20Problem.pdf |
| work_keys_str_mv | AT ezzarahmedmohamedalaaeldin antcolonyoptimizationapproachesforthesequentialorderingproblem |