Benchmark testing of simulated annealing, adaptive random search and genetic algorithms for the global optimization of bioprocesses

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Abstract

This paper studies the global optimisation of bioprocesses employing model-based dynamic programming schemes. Three stochastic optimisation algorithms were tested: simulated annealing, adaptive random search and genetic algorithms. The methods were employed for optimising two challenging optimal control problems of fed-batch bioreactors. The main results show that adaptive random search and genetic algorithms are superior at solving these problems than the simulated annealing based method, both in accuracy and in the number of function evaluations.
Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms
Subtitle of host publicationProceedings of the International Conference in Coimbra, Portugal, 2005
EditorsBernardete Ribeiro, Rudolf F. Albrecht, Andrej Dobnikar, David W. Pearson, Nigel C. Steele
Place of PublicationVienna
PublisherSpringer
Pages292-295
Number of pages4
ISBN (Electronic)978-3-211-27389-0
ISBN (Print)3-211-24934-6, 978-3-211-24934-5
DOIs
Publication statusPublished - 2005
Event7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA) - Coimbra, Portugal
Duration: 21 Mar 200523 Mar 2005

Publication series

NameSpringer Computer Science

Conference

Conference7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA)
CountryPortugal
CityCoimbra
Period21/03/0523/03/05

Keywords

  • Genetic Algorithm
  • Simulated Annealing
  • Dynamic Optimization
  • Stochastic Algorithm
  • Material Balance Equation

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