Artificial Bee Colony Algorithm for Optimizing Enterprise Network Topology Design with Multiple Objectives
| Author | Raheej, Amani D. Saad | |
|---|---|---|
| metadata.dc.contributor.department | UNIVERSITY OF BAHRAIN College of Information Technology | |
| metadata.dc.date.accessioned | 2026-09-20T15:49:21Z | |
| Publication date | 2015 | |
| Degree level | Master | |
| Abstract | Abstract The problem of topological design of distributed local area networks (DLANs) is a combinatorial NP-hard problem. The problem complexity is further magnified by the presence of multiple objectives, which may be conflicting in nature and require optimization systematically and simultaneously. Improvement in one of the objectives may lead to a certain level of degradation in one or more other objectives. In this thesis, the problem of DLAN tree topology design considers five design objectives: reliability, network availability, average link utilization, average network delay, and monetary cost. There are two aspects of the problem under consideration. The first is the multi-objectivity, and the second is the optimization. The optimal design is governed by both issues Historically, many techniques have been proposed over the years to handle the multi-objective aspects of an optimization problem. In this thesis, Goal Programming (GP) is adopted to combine the five design objectives into a single overall objective function. The second aspect of the problem is the algorithm that performs the optimization process. Swarm intelligent techniques are proven to be able to solve complex problems within reasonable computational time and thus have been extensively deployed to solve variants of NP-hard problems. In this thesis, the artificial bee colony optimization algorithm (ABC) is adapted and employed. To ascertain the performance of the adapted ABC algorithm, the ant colony optimization algorithm (ACO) is also adapted for the underlying problem as another swarm-based algorithm to facilitate the intended comparison. In the results, ACO outperformed ABC for test cases with relatively smaller problem dimensions, while ABC performed better for larger dimensions. The results also showed that ABC is a high-performing algorithm with powerful search capabilities and could be tailored to suit large problem dimensions | |
| metadata.dc.identifier.other | Drupal node 3902 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/artificial-bee-colony-algorithm-optimizing-enterprise-network-topology-design-multiple | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6871 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2025-07/001279_artificial_bee_colony_algorithm_for_optimizing_enterprise_network_topology_design_with_multiple_objectives.pdf | |
| Subject | Computer networks -- Design and construction -- Dissertations | |
| Subject | Computer networks -- Mathematical models -- Dissertations | |
| Subject | Thesis (Master) | |
| Title | Artificial Bee Colony Algorithm for Optimizing Enterprise Network Topology Design with Multiple Objectives | |
| Type | Master's Thesis |
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