Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/4025
Título: Penalty fuzzy function for derivative-free optimization
Autor: Matias, João
Mestre, Pedro
Correia, Aldina
Couto, Pedro
Serôdio, Carlos
Melo-Pinto, P.
Data: 2012
Editora: Springer
Relatório da Série N.º: Advances in Intelligent and Soft Computing; Vol. 107
Resumo: Penalty and Barrier methods are normally used to solve Nonlinear Optimization Problems constrained problems. The problems appear in areas such as engineering and are often characterised by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot net used. A Java based API was implemented, including only derivative-free optimizationmethods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalisation when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the prosed penalty function besides being very robust also exhibits a very good performance.
Peer review: yes
URI: http://hdl.handle.net/10400.22/4025
ISBN: 978-3-642-24000-3
978-3-642-24001-0
ISSN: 1867-5662
Versão do Editor: http://link.springer.com/chapter/10.1007%2F978-3-642-24001-0_27
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