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.
Keywords
- Pareto Front
- Multiobjective Optimization
- Pareto Frontier
- Multiobjective Evolutionary Algorithm
- Normalize Normal Constraint
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Partially supported by MEC (Spanish government) and FEDER funds: projects DPI2005-07835, DPI2004-8383-C03-02 and GVA-026.
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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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