Well-distributed Pareto front by using the ∉-MOGA evolutionary algorithm

Authors:
J. M. Herrero;M. Martínez;J. Sanchis;X. Blasco
Affiliations:
Department of Systems Engineering and Control, Polytechnic University of Valencia, Spain;Department of Systems Engineering and Control, Polytechnic University of Valencia, Spain;Department of Systems Engineering and Control, Polytechnic University of Valencia, Spain;Department of Systems Engineering and Control, Polytechnic University of Valencia, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 2
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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 ∉-MOGA, has been designed to converge towards ΘP*, a reduced but well distributed representation of the Pareto set ΘP. The algorithm achieves good convergence and distribution of the Pareto front J(ΘP) 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.