Reference : A hybrid optimization technique coupling an evolutionary and a local search algorithm
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/2268/19956
A hybrid optimization technique coupling an evolutionary and a local search algorithm
English
Kelner, Vincent mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
Capitanescu, Florin mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Léonard, Olivier mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2006
Journal of Computational & Applied Mathematics
Elsevier Science
215
2
281-287
Yes (verified by ORBi)
International
0377-0427
Amsterdam
The Netherlands
[en] Nonlinear programming ; Genetic algorithm ; Interior point method
[fr] Multiobjective optimization
[en] 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.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/19956
10.1016/j.cam.2006.03.048

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