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dc.contributor.advisorHanna, Darrin
dc.contributor.authorLohrer, Michael
dc.date.accessioned2013-05-29T17:10:34Z
dc.date.available2013-05-29T17:10:34Z
dc.date.issued2013-05-29
dc.identifier.urihttp://hdl.handle.net/10323/1602
dc.description.abstractWhen a problem is large or difficult to solve, computers are often used to find the solution. But when the problem becomes too large, traditional methods of finding the answer may not be enough. It is in turning to nature that inspiration can be found to solve these difficult problems. Artificial intelligence seeks to emulate creatures and processes found in nature, and turn their techniques for solving a problem into an algorithm. Many such metaheuristic algorithms have been developed, but there is a continuous search for better, faster algorithms. The recently developed Firefly Algorithm has been shown to outperform the longstanding Particle Swarm Optimization, and this work aims to verify those results and improve upon them by comparing the two algorithms with a large scale application. A direct hardware implementation of the Firefly Algorithm is also proposed, to speed up performance in embedded systems applications.en_US
dc.subjectFirefly Algorithmen_US
dc.subjectSwarm Optimizationen_US
dc.subjectEmission Source Localizationen_US
dc.subjectMetaheuristicsen_US
dc.titleA Comparison Between the Firefly Algorithm and Particle Swarm Optimizationen_US
dc.typeThesiseng


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