Reference : A hybrid optimization technique coupling evolutionary and local search algorithms
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/2268/12669
A hybrid optimization technique coupling evolutionary and local search algorithms
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 >]
Jun-2008
Journal of Computational & Applied Mathematics
Elsevier Science
215
2
448-456
Yes (verified by ORBi)
International
0377-0427
Amsterdam
The Netherlands
[en] genetic algorithm ; interior-point method ; multi-objective 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.
RW PIGALL project
http://hdl.handle.net/2268/12669
10.1016/j.cam.2006.03.048

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