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Journal of Materials Science and Nanotechnology | Volume 3
February 25-26, 2019 | Paris, France
Materials Science and Engineering
2
nd
International Conference on
Global optimization methods and techniques in Engineering design
Jüri Majak, Meelis Pohlak, Martin Eerme, Kristo Karjust
and
Fabio Auriemma
Tallinn University of Technology, Estonia
T
raditional gradient based optimization methods and
techniques are still most widely used tools in engineering
design. However, the nature of the real world engineering
design problems cannot be often well covered by traditional
techniques due to precence of integer and discrete variables,
a number of local extremes, multiple optimality criteria, etc.
Global optimization methods (GOM) and techniques have
property to escape the local extreme and have a better global
perspective than the traditional gradient based methods. GOM
allow to omit computing derivatives. The cost needed to pay for
more powerfull methods is fact that the GOMmanipulate with
population instead of single solution leading to time consuming
numerous evaluations of objective functions. Most commonly
the meta-models are utilized for reducinhg computational
cost. Continuous improvement of GOM methods and tools,
One key issue is decomposition of complex engineering design
problems into simpler sub-tasks leading as rule to reduction
of complexity and computing time. In the current study are
covered hierarchical multi-criteria optimization algorithms
and procedures developed by workgroup for solving wide
class of practical and theoretical engineering design problems
like design of smart composites with structural health
monitoring capabilities, design of a slotless permanent magnet
generator for wind turbine, design of car frontal protection
system, optimal material orientations problems for linear
elastic 3D orthotropic materials, etc. These solutions are
optimized taking account the features of particular problems,
combining GOM tools (hybrid methods, etc.) , utilizing meta-
modelling techniques (in most cases artifial neural networks).
e:
juri.majak@taltech.ee