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Remarks on the efficiency of bionic optimisation strategies

  • Bionic optimisation is one of the most popular and efficient applications of bionic engineering. As there are many different approaches and terms being used, we try to come up with a structuring of the strategies and compare the efficiency of the different methods. The methods mostly proposed in literature may be classified into evolutionary, particle swarm and artificial neural net optimisation. Some related classes have to be mentioned as the non-sexual fern optimisation and the response surfaces, which are close to the neuron nets. To come up with a measure of the efficiency that allows to take into account some of the published results the technical optimisation problems were derived from the ones given in literature. They deal with elastic studies of frame structures, as the computing time for each individual is very short. General proposals, which approach to use may not be given. It seems to be a good idea to learn about the applicability of the different methods at different problem classes and then do the optimisation according to these experiences. Furthermore in many cases there is some evidence that switching from one method to another improves the performance. Finally the identification of the exact position of the optimum by gradient methods is often more efficient than long random walks around local maxima.

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Metadaten
Author of HS ReutlingenGekeler, Simon; Steinbuch, Rolf
URL:http://www.davidpublisher.org/index.php/Home/Article/index?id=7144.html
ISSN:2159-5291
eISSN:2159-5305
Erschienen in:Journal of mathematics and system science
Publisher:David Publishing
Place of publication:Libertyville, Ill.
Document Type:Journal article
Language:English
Publication year:2014
Tag:artificial neural nets; bionic optimisation; efficiency; evolutionary optimisation; particle swarm optimisation
Volume:4
Issue:3
Page Number:16
First Page:139
Last Page:154
DDC classes:570 Biowissenschaften, Biologie
Open access?:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell