Use of Multiobjective Optimization Concepts to Handle Constraints in Single-Objective Optimization

Arturo Hernández Aguirre¹, Salvador Botello Rionda¹, Carlos A. Coello Coello², and Giovanni Lizárraga Lizárraga¹

¹Center for Research in Mathematics (CIMAT)
Department of Computer Science
Guanajuato, Gto. 36240, México
{artha,botello,giovanni}@cimat.mx

²CINVESTAV-IPN
Evolutionary Computation Group
Depto. de Ingeniería Eléctrica
Sección de Computación
Av. Instituto Politécnico Nacional No. 2508
Col. San Pedro Zacatenco
México, D. F. 07300
ccoello@cs.cinvestav.mx

Abstract. In this paper, we propose a new constraint-handling technique for evolutionary algorithms which is based on multiobjective optimization concepts. The approach uses Pareto dominance as its selection criterion, and it incorporates a secondary population. The new technique is compared with respect to an approach representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. Results indicate that the proposed approach is able to match and even outperform the technique with respect to which it was compared at a lower computational cost.

LNCS 2723, p. 573 ff.

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