Title :
Self-adaptive Hybrid differential evolution with simulated annealing algorithm for numerical optimization
Author :
Hu, Zhong-bo ; Su, Qing-hua ; Xiong, Sheng-wu ; Hu, Fu-gao
Author_Institution :
Dept. of Math., Xiaogan Univ., Xiaogan
Abstract :
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.
Keywords :
learning systems; self-adjusting systems; simulated annealing; SaDESA algorithm; global optimization; learning strategy; numerical optimization; real parameter optimization; self-adaptive hybrid differential evolution; simulated annealing algorithm; Chromium; Computational modeling; Computer science; Encoding; Genetic mutations; Mathematics; Robust control; Simulated annealing; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4630947
