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dc.contributor.authorEl-Abd, Mohammed
dc.date.accessioned2016-04-07T08:38:14Z
dc.date.available2016-04-07T08:38:14Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11675/801
dc.description.abstractThe class of foraging algorithms is a relatively new field based on mimicking the foraging behavior of animals, insects, birds or fish in order to develop efficient optimization algorithms. The artificial bee colony (ABC), the bees algorithm (BA), ant colony optimization (ACO), and bacterial foraging optimization algorithms (BFOA) are examples of this class to name a few. This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. To the best of our knowledge, most of the work conducted so far using foraging algorithms has been tested on classical functions. This work provides the comparison using the well-known CEC05 benchmark functions based on the solution reached, the success rate, and the performance rate.
dc.relation.journalInformation Sciences, Elsevier
dc.titlePerformance Assessment of Foraging Algorithms vs. Evolutionary Algorithms
dc.typeJournal Article
dc.journal.volume182
dc.journal.issue1
dc.article.pages243-263
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0020025511004555


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