Well-Distributed Pareto Front by Using the \(\epsilon \hskip-0.9em \nearrow \hskip-0.4em-MOGA\) Evolutionary Algorithm

  • J. M. Herrero
  • M. Martínez
  • J. Sanchis
  • X. Blasco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


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.


Pareto Front Multiobjective Optimization Pareto Frontier Multiobjective Evolutionary Algorithm Normalize Normal Constraint 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. M. Herrero
    • 1
  • M. Martínez
    • 1
  • J. Sanchis
    • 1
  • X. Blasco
    • 1
  1. 1.Department of Systems Engineering and Control, Polytechnic University of ValenciaSpain

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