Interacted Multiple Ant Colonies for Search Stagnation Problem
Ant Colony Optimization (ACO) is a successful application of swarm intelligence. ACO algorithms generate a good solution at the early stages of the algorithm execution but unfortunately let all ants speedily converge to an unimproved solution. This thesis addresses the issues associated with search...
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| Format: | Thesis |
| Language: | en en |
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2010
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| Online Access: | https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/ |
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| _version_ | 1833435795003801600 |
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| author | Aljanabi, Alaa Ismael |
| author_facet | Aljanabi, Alaa Ismael |
| author_sort | Aljanabi, Alaa Ismael |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| description | Ant Colony Optimization (ACO) is a successful application of swarm intelligence. ACO algorithms generate a good solution at the early stages of the algorithm execution but unfortunately let all ants speedily converge to an unimproved solution. This thesis addresses the issues associated with search stagnation problem that ACO algorithms suffer from. In particular, it proposes the use of multiple interacted ant colonies as a new algorithmic framework. The proposed framework is incorporated with necessary mechanisms that coordinate the work of these colonies to avoid stagnation situations and therefore achieve a better performance compared to one colony ant algorithm. The proposed algorithmic framework has been experimentally tested on two different NP-hard combinatorial optimization problems, namely the travelling salesman problem and the single machine total weighted tardiness problem. The experimental results show the superiority of the proposed approach than existing one colony ant algorithms like the ant colony system and max-min ant system. An analysis study of the stagnation behaviour shows that the proposed algorithmic framework suffers less from stagnation than other ACO algorithmic frameworks. |
| format | Thesis |
| id | my.uum.etd-2111 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2010 |
| record_format | eprints |
| spelling | my.uum.etd-21112022-04-10T06:13:53Z https://etd.uum.edu.my/2111/ Interacted Multiple Ant Colonies for Search Stagnation Problem Aljanabi, Alaa Ismael QA299.6-433 Analysis Ant Colony Optimization (ACO) is a successful application of swarm intelligence. ACO algorithms generate a good solution at the early stages of the algorithm execution but unfortunately let all ants speedily converge to an unimproved solution. This thesis addresses the issues associated with search stagnation problem that ACO algorithms suffer from. In particular, it proposes the use of multiple interacted ant colonies as a new algorithmic framework. The proposed framework is incorporated with necessary mechanisms that coordinate the work of these colonies to avoid stagnation situations and therefore achieve a better performance compared to one colony ant algorithm. The proposed algorithmic framework has been experimentally tested on two different NP-hard combinatorial optimization problems, namely the travelling salesman problem and the single machine total weighted tardiness problem. The experimental results show the superiority of the proposed approach than existing one colony ant algorithms like the ant colony system and max-min ant system. An analysis study of the stagnation behaviour shows that the proposed algorithmic framework suffers less from stagnation than other ACO algorithmic frameworks. 2010-01 Thesis NonPeerReviewed text en https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf text en https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf Aljanabi, Alaa Ismael (2010) Interacted Multiple Ant Colonies for Search Stagnation Problem. PhD. thesis, Universiti Utara Malaysia. |
| spellingShingle | QA299.6-433 Analysis Aljanabi, Alaa Ismael Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title | Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_full | Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_fullStr | Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_full_unstemmed | Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_short | Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_sort | interacted multiple ant colonies for search stagnation problem |
| topic | QA299.6-433 Analysis |
| url | https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/ |
| url_provider | http://etd.uum.edu.my/ |