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Differential Evolution: A Survey and Analysis

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dc.contributor.author Eltaeib, Tarik
dc.contributor.author Mahmood, Ausif
dc.date.accessioned 2019-07-15T17:21:23Z
dc.date.available 2019-07-15T17:21:23Z
dc.date.issued 2018-10-16
dc.identifier.citation Eltaeib, T.; Mahmood, A. Differential Evolution: A Survey and Analysis. Applied Sciences. 2018, 8, 1945. en_US
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4205
dc.description.abstract Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. en_US
dc.description.uri http://dx.doi.org/10.3390/app8101945
dc.language.iso en_US en_US
dc.publisher MDPI en_US
dc.subject Differential evolution en_US
dc.subject Optimization en_US
dc.subject Stochastic en_US
dc.title Differential Evolution: A Survey and Analysis en_US
dc.type Article en_US
dc.publication.issue 10 en_US
dc.publication.name Applied Sciences en_US
dc.publication.volume 8 en_US

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