Abstract
In the field of multiobjective optimization, important efforts have been made in recent years to generate global Pareto fronts uniformly distributed. A new multiobjective evolutionary algorithm, called \(\epsilon \hskip-0.9em \nearrow \hskip-0.4em-MOGA\), has been designed to converge towards \(\mathbf{\Theta}_P^*\), a reduced but well distributed representation of the Pareto set Θ P . The algorithm achieves good convergence and distribution of the Pareto front J(Θ P ) with bounded memory requirements which are established with one of its parameters. Finally, a optimization problem of a three-bar truss is presented to illustrate the algorithm performance.
Partially supported by MEC (Spanish government) and FEDER funds: projects DPI2005-07835, DPI2004-8383-C03-02 and GVA-026.
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References
Batill, S.M.: Course: ME/AE 446. Finite Element Methods in Structural Analysis, Planar truss applications (1995), http://www.nd.edu
Coello, C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in applied Mechanics and Engineering 191, 1245–1287 (2002)
Coello, C., Veldhuizen, D., Lamont, G.: Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, Boston (2002)
Herrero, J.M.: Non-linear Robust identification using evolutionary algorithms, PhD thesis, Polytechnic University of Valencia (2006)
Herrero, J.M., Blasco, X., Martínez, M., Sanchis, J.: Robust identification of a biomedical process by evolutionary algorithms. RIAI 3(4), 75–86 (2006)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multi-objective optimization. Evolutionary computation 10(3), 263–282 (2002)
Martínez, M., Blasco, X., Sanchis, J.: Global and well-distributed pareto frontier by modified normalized constraint methods. Struct. Multidisc. Optim. 34(3) (2006), doi:10.1007/S00158-006-0071-5
Messac, A., Ismail, A., Mattson, C.A.: The normalized normal constraint method for generating the Pareto frontier. Struct. Multidisc. Optim. 25, 86–98 (2003)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: Methods and applications, PhD thesis, Swiss Federal Institute of Technology Zurich (1999)
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Herrero, J.M., Martínez, M., Sanchis, J., Blasco, X. (2007). Well-Distributed Pareto Front by Using the \(\epsilon \hskip-0.9em \nearrow \hskip-0.4em-MOGA\) Evolutionary Algorithm . In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_36
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DOI: https://doi.org/10.1007/978-3-540-73007-1_36
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