A Comparison Between the Firefly Algorithm and Particle Swarm Optimization

dc.contributor.advisor	Hanna, Darrin
dc.contributor.author	Lohrer, Michael
dc.date.accessioned	2013-05-29T17:10:34Z
dc.date.available	2013-05-29T17:10:34Z
dc.date.issued	2013-05-29
dc.description.abstract	When 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.identifier.uri	http://hdl.handle.net/10323/1602
dc.subject	Firefly agorithm	en_US
dc.subject	Swarm optimization	en_US
dc.subject	Emission source localization	en_US
dc.subject	Metaheuristics	en_US
dc.title	A Comparison Between the Firefly Algorithm and Particle Swarm Optimization	en_US
dc.type	Thesis	eng

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