On The Efficiency Of Bionic Optimisation
Procedures
Author(s)
S. Gekeler, R. Steinbuch & C. Widmann
Abstract
Bionic optimisation strategies have proven to be efficient in many applications
especially if there are many local maxima to be expected in parameter spaces of
higher dimensions. In structural mechanics, the central question is whether one
particular procedure is to be preferred generally or if there are different problem
types where some procedures are more efficient than others. Evolutionary
optimisation with some sub-strategies, particle swarm optimisation, and neural
nets along with hybrid approaches that couple the aforementioned methods have
been investigated to some extent. These approaches are not uniquely defined, but
rather imply many variants regarding the definition and selection of nextgeneration
members, varying parameters of the underlying processes and the
criteria to switch the strategy. To measure the performance of the different
approaches some simple test examples have been used. The indicator of the
procedures performance was the number of individuals which needed to be
studied in order to come up with a satisfactory solution. As our main concern
was about problems with many optimisation parameters, artificial neural nets do
not show sufficient convergence velocities in our class of optimisation studies.
Evolutionary optimisation, its subclass of fern optimisation and particle swarm
optimisation prove to be of comparable power when applied to the test problems.
It should not be disregarded that for all these approaches some experience about
the optimisation parameters has to be gathered. In consequence, the total number
of runs or individuals necessary to do the final optimisation is essentially larger
than the number of runs during this final optimisation. Good initial proposals
prove to be the most important source for all optimisation processes.
Keywords: bionic optimisation, evolutionary optimisation, particle swarm
optimisation, performance, structural mechanics.
Keywords
bionic optimisation, evolutionary optimisation, particle swarm
optimisation, performance, structural mechanics.
Related Book
Other papers in this volume
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