Integration of genetic optimisation and neuro-fuzzy approximation in parametric engineering design

  • Authors:

  • Kostas Saridakis;Argyris Dentsoras

  • Affiliations:

  • Machine Design Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece;Machine Design Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

  • Venue:
  • International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
  • Year:
  • 2009

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Abstract

The present article introduces a systematic approach that applies soft computing techniques, such as genetic algorithms and neuro-fuzzy approximation, in parametric engineering design, such as genetic algorithms and neuro-fuzzy approximation. A generic design problem representation is utilised and genetic optimisation is used in order to extract the optimal design solution based on customised optimisation criteria. Apart from the extraction of the optimal solution, the genetic optimisation creates records of elite solutions that can be reused as meta-knowledge to enhance the design results in the context of different frameworks. The efficiency of the proposed neuro-fuzzy approximated models and the meta-knowledge supported architectures is evaluated against the conventional and analytical models, on the basis of an example case of parametric design of oscillating conveyors.