Parallel Function Optimisation Using Evolutionary Algorithms and Deterministic Neighbourhood Search

dc.contributor.author	Zgeras, Ioannis
dc.contributor.author	Brehm, Jürgen
dc.contributor.author	Reisch, Andreas
dc.date.accessioned	2017-12-06T09:06:17Z
dc.date.available	2017-12-06T09:06:17Z
dc.date.issued	2011
dc.description.abstract	Modern computer hardware provides massive computational power by parallelism. However, many of the existing algorithms and frameworks are optimised for sequential execution and are not capable to be parallelised or do not scale well on complex parallel architectures. In our paper, we present a metaheuristic consisting of a parallel Evolutionary Algorithm and a parallel Neighbourhood Search. For the implementation massively parallel GPUs are used. This framework is evaluated on the application of function optimisation.	en
dc.identifier.doi	10.1007/BF03341994
dc.identifier.pissn	0177-0454
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/8563
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 28, No. 1
dc.relation.ispartofseries	PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware
dc.subject	Particle Swarm Optimization
dc.subject	Evolutionary Algorithm
dc.subject	Graphic Processing Unit
dc.subject	Function Optimisation
dc.subject	Variable Neighbourhood Search
dc.title	Parallel Function Optimisation Using Evolutionary Algorithms and Deterministic Neighbourhood Search	en
dc.type	Text/Journal Article
gi.citation.endPage	156
gi.citation.startPage	152

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