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https://ptsldigital.ukm.my/jspui/handle/123456789/513463Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Salwani Abdullah, Assoc. Prof. Dr. | |
| dc.contributor.author | Malek Ali Ahmad Fehaid (P49063) | |
| dc.date.accessioned | 2023-10-16T04:36:57Z | - |
| dc.date.available | 2023-10-16T04:36:57Z | - |
| dc.date.issued | 2012-05 | |
| dc.identifier.other | ukmvital:73407 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/513463 | - |
| dc.description | Examination timetabling problems (ETTPs) become an intensive research in optimisation field. In ETTP, examinations are scheduled to timeslots and rooms based on the constraints and universities resources. Many approaches in the literature have addressed this difficult optimisation problem. Literature review shows that most of the population-based meta-heuristic approaches concentrated in finding only one high quality solution. This motivated the investigation of honeybee algorithms that are based on foreign behaviour (Honeybee Based Foraging Behaviour, HBFB) for ETTPs that try to bring all the solution in the population to be as good as possible. To date, HBFB algorithms have not been applied to the ETTPs. HBFB algorithms mimic the real foreign behaviour of honeybee in searching for food sources. HBFB algorithms are population based algorithms which consist of three main processes i.e. exploration, selection and exploitation. Some of the important challenges include finding a right selection strategy that bring the population to converge together without sacrificing the quality of the solutions, deal with adaptive mechanism for a better exploitation during the search process, and creating a balance between the exploration and exploitation to avoid premature convergence and get trapped in a local optimum. The research firstly aims to drive the population to better solutions by considering the principles of selection strategies. Secondly, to enhance the exploration of the search space by adaptively change the neighbourhood operator during the search process. Finally, to balance the exploration and exploitation processes, overcome the premature convergence and avoid from easily trapped into a local optimum by employing hybridisation methods. Towards these aims, three different models of the foreign behaviour in honeybee algorithms i.e. Artificial Bee Colony (ABC), Bees Algorithm (BA) and Bee Colony Optimisation (BCO) have been proposed and tested on two categories of datasets i.e. uncapacitated examination timetabling and International Timetabling Competition datasets (ITC2007). Three selection strategies, namely, tournament, rank and disruptive have been tested. The results demonstrate that the disruptive selection performed better than tournament and rank selections when embedded with ABC, BA and BCO (coded as DABC, DBA and DBCO, respectively). In addition, a self-adaptive mechanism for the neighbourhood search has been employed within DABC, DBA and DBCO algorithms and able to improve the quality of the solution (coded as Self-Adaptive DABC, Self-Adaptive DBA, Self-Adaptive DBCO). These are then incorporated with two local search algorithms (i.e. Simulated Annealing, SA and Late Acceptance Hill Climbing, LAHC) which show that the LAHC can further enhance the quality of the solutions in comparison with SA (coded as Self-Adaptive DABCLAHC, Self-Adaptive DBALAHC, Self-Adaptive DBCOLAHC). Overall comparisons indicate that Self-Adaptive DBCOLAHC works well across all datasets and able to obtain two best results in comparison with best known results in the literature particularly on the uncapacitated examination timetabling problem.,PhD | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Honeybee algorithms | |
| dc.subject | Foraging behaviour | |
| dc.subject | Examination timetabling problems | |
| dc.subject | Evolutionary computation | |
| dc.title | Honeybee algorithms based on foraging behaviour for examination timetabling problems | |
| dc.type | Theses | |
| dc.format.pages | 160 | |
| dc.identifier.callno | QA76.618 .F437 2012 3 | |
| dc.identifier.barcode | 000523 | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_73407+Source01+Source010.PDF Restricted Access | 5.09 MB | Adobe PDF |  View/Open |
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