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...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | eng eng |
| منشور في: |
2010
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| الوصول للمادة أونلاين: | https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf |
| الوسوم: |
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my-uum-etd.21112022-04-10T06:13:53Z Interacted Multiple Ant Colonies for Search Stagnation Problem 2010-01 Aljanabi, Alaa Ismael Ku Mahamud , Ku Ruhana Md. Norwawi, Norita College of Arts and Sciences (CAS) College of Arts and Sciences 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 https://etd.uum.edu.my/2111/ https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf text eng public https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia |
| institution |
Universiti Utara Malaysia |
| collection |
UUM ETD |
| language |
eng eng |
| advisor |
Ku Mahamud , Ku Ruhana Md. Norwawi, Norita |
| topic |
QA299.6-433 Analysis |
| spellingShingle |
QA299.6-433 Analysis Aljanabi, Alaa Ismael Interacted Multiple Ant Colonies for Search Stagnation Problem |
| 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 |
| qualification_name |
Ph.D. |
| qualification_level |
Doctorate |
| author |
Aljanabi, Alaa Ismael |
| author_facet |
Aljanabi, Alaa Ismael |
| author_sort |
Aljanabi, Alaa Ismael |
| title |
Interacted Multiple Ant Colonies for Search Stagnation Problem |
| title_short |
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_sort |
interacted multiple ant colonies for search stagnation problem |
| granting_institution |
Universiti Utara Malaysia |
| granting_department |
College of Arts and Sciences (CAS) |
| publishDate |
2010 |
| url |
https://etd.uum.edu.my/2111/1/Alaa_Ismael_Aljanabi.pdf https://etd.uum.edu.my/2111/2/1.Alaa_Ismael_Aljanabi.pdf |
| _version_ |
1747827267180953600 |