Efficient Genetic Algorithms for Solving Hard Constrained Optimization Problems

Bruno Sareni (1, 2) , Laurent Krähenbühl (1, 3) , Alain Nicolas (1, 3)

1 CEGELY - Centre de génie électrique de Lyon
2 LEEI - Laboratoire électrotechnique et électronique industrielle
3 AMPERE - Ampère

This paper studies many Genetic Algorithm strategies to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to showadvantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation method.

Mots clés: constrained optimization methods; genetic algorithms; niching methods; shape optimization methods

Domaines: Autre; Electromagnétisme; Optimisation et contrôle [math.OC]

Bruno Sareni, Laurent Krähenbühl, Alain Nicolas. Efficient Genetic Algorithms for Solving Hard Constrained Optimization Problems. IEEE Transactions on Magnetics, 2000, 36 (4 Part 1), pp.1027-1030. ⟨10.1109/20.877616⟩. ⟨hal-00135911⟩