A common type of problems that exist in both industrial and scientific spaces are optimization problems. These problems can be found in among other things manufacturing, pathfinding, network routing and more. Because of the wide area of application, optimization is well a studied area. One solution to these types of problems is the Ant Colony Optimization algorithm that has been around since 1991 and has undergone a lot of developments over the years. This algorithm draws inspiration from real ant colonies and their procedure for foraging. However, a common criticism of this algorithm is its poor scalability. To tackle the scalability problem this thesis will combine the concept of binary space partitioning with the Ant Colony Optimization algorithm. The goal is to examine the algorithms convergence times and lengths of the paths produced. The results are measured in intervals by calculating the best possible path found at every interval. The findings showed that given an unlimited execution time the original Ant Colony Optimization algorithm produced shorter paths. But when a limit on execution time was introduced and the problem sizes grew the performance began to favor the partitioned versions. These findings could be useful in areas where complex optimization problems need to be solved within a limited timeframe. / <p>The presentation took place via an online conference call using the software "Zoom"</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-55049 |
| Date | January 2021 |
| Creators | Ståhlbom, Niclas |
| Publisher | Mälardalens högskola, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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