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Self-adaptive Hybrid differential evolution with simulated annealing algorithm for numerical optimization

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4 Author(s)
Zhong-bo Hu ; Dept. of Math., Xiaogan Univ., Xiaogan ; Qing-hua Su ; Sheng-wu Xiong ; Fu-gao Hu

A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the SaDESA is evaluated on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization. Comparative study exposes the SaDESA algorithm as a competitive algorithm for a global optimization.

Published in:

Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on

Date of Conference:

1-6 June 2008