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Multi‐objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches
Author(s) -
GasparCunha A.,
Mendes F.,
Costa M. F. P.
Publication year - 2011
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.2010.00782.x
Subject(s) - memetic algorithm , computer science , artificial neural network , pareto principle , local search (optimization) , evolutionary algorithm , mathematical optimization , multi objective optimization , filter (signal processing) , computation , evolutionary computation , artificial intelligence , algorithm , machine learning , mathematics , computer vision
In this work, two methodologies to reduce the computation time of expensive multi‐objective optimization problems are compared. These methodologies consist of the hybridization of a multi‐objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.