A hybrid optimization technique coupling an evolutionary and a local search algorithm

Authors:
Vincent Kelner;Florin Capitanescu;Olivier Léonard;Louis Wehenkel
Affiliations:
Department of Aerospace, Mechanical and Materials Engineering Sciences, Turbomachinery Group, University of Liège, Chemin des Chevreuils 1, 4000 Liège, Belgium;Department of Electrical Engineering and Computer Science Stochastic Methods, University of Liège, Montefiore Institute, 4000 Liège, Belgium;Department of Aerospace, Mechanical and Materials Engineering Sciences, Turbomachinery Group, University of Liège, Chemin des Chevreuils 1, 4000 Liège, Belgium;Department of Electrical Engineering and Computer Science Stochastic Methods, University of Liège, Montefiore Institute, 4000 Liège, Belgium
Venue:
Journal of Computational and Applied Mathematics
Year:
2008

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Abstract

Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case.