Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Optimization Techniques in Unfolding |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | This thesis presents an in-depth exploration of optimization algorithms aimed at addressing the challenging problem of unfolding 3D meshes by removing overlaps from initial unfoldings. Four distinct algorithms were selected for investigation: iterated local search (ILS), stochastic hill climbing (SHC), adaptive step size random search (ASSRS), and adaptive stochastic hill climbing (ASHC). Through implementation and experimentation, the performance of each algorithm was analyzed across varying mesh sizes and complexities. In the course of investigation, it became apparent that ILS struggled to deliver effective and efficient solutions, primarily due to its simplistic approach. ASSRS, a promising concept, faced challenges in its execution, with significant fail rates and a dependence on basic local search strategies. SHC, incorporating randomness to overcome local optima, demonstrated solid performance with success rates exceeding 93\% and competitive runtimes. Notably, ASHC emerged as the standout algorithm, enhancing SHC through adaptive probabilities of making unfavorable moves as overlap counts decrease. ASHC consistently outperformed the other algorithms, showcasing the potential of adaptiveness in computational unfolding. Comparison with related works revealed ASHC's competitive edge, outperforming simulated annealing and performing on par with a genetic algorithm. As a result, this thesis contributes valuable insights into the realm of 3D mesh unfolding optimization, paving the way for future refinements of ASHC and potential advancements in the unfolding of complex 3D structures. |
| PDF File |
Download
|
| Export |
BibTeX
|