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  • The goal of this paper is to investigate under which settings it is effective to use an asynchronous version of the master-slave model of a population-based optimization algorithm. Both versions, synchronous and asynchronous, are implemented and compared using data from the multi-objective identification of parameters for a material constitutive law for concrete. Results have shown that the synchronous version is preferable for the low number of processors whereas for the high number of available CPUs the asynchronous version is clear winner.
  • The goal of this paper is to investigate under which settings it is effective to use an asynchronous version of the master-slave model of a population-based optimization algorithm. Both versions, synchronous and asynchronous, are implemented and compared using data from the multi-objective identification of parameters for a material constitutive law for concrete. Results have shown that the synchronous version is preferable for the low number of processors whereas for the high number of available CPUs the asynchronous version is clear winner. (en)
Title
  • Load-balancing of Master-Slave Evolutionary Algorithm for Parameters Identification
  • Load-balancing of Master-Slave Evolutionary Algorithm for Parameters Identification (en)
skos:prefLabel
  • Load-balancing of Master-Slave Evolutionary Algorithm for Parameters Identification
  • Load-balancing of Master-Slave Evolutionary Algorithm for Parameters Identification (en)
skos:notation
  • RIV/68407700:21110/09:00157924!RIV10-MPO-21110___
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  • P(FT-TA4/100)
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  • 323965
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  • RIV/68407700:21110/09:00157924
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  • optimization; parallel evolutionary algorithms; load-balancing; parameters identification; inverse analysis; parallel computing (en)
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  • [D93F46DE0865]
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  • Pecs
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  • Stirling
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  • Proceedings of the First International Conference on Parallel, Distributed and Grid Computing for Engineering
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  • Lepš, Matěj
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  • 000271452700010
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issn
  • 1759-3433
number of pages
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  • Civil-Comp Press Ltd
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  • 978-1-905088-29-4
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  • 21110
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