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

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:215

Article; Proceedings Paper

Kelner, Vincent¹  Capitanescu, Florin²  Uonard, Olivier¹  Wehenkel, Louis² 

[1] Univ Liege, Dept Aerosp Mech & Mat Engn Sci, Turbomachinery Grp, B-4000 Liege, Belgium

[2] Univ Liege, Dept Elect Engn & Comp Sci Stochast Methods, B-4000 Liege, Belgium

关键词: nonlinear programming;    genetic algorithm;    interior point method;    multiobjective optimization;   

DOI  :  10.1016/j.cam.2006.03.048

来源: Elsevier

【 摘 要 】

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. (C) 2007 Elsevier B.V. All rights reserved.

【 授权许可】

Free