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A Differential Evolution with Simulated Annealing Updating Method

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3 Author(s)
Jing-Yu Yan ; Autom. Dept., Univ. of Sci. & Technol. of China, Hefei ; Qing Ling ; De-Min Sun

In this paper, we point out that conventional differential evolution (CDE) algorithm runs the risk of being trapped by local optima because of its greedy updating strategy and intrinsic differential property. A novel simulated annealing differential evolution (SADE) algorithm is proposed to improve the premature property of CDE. With the aid of simulated annealing updating strategy, SADE is able to escape from the local optima, and achieve the balance between exploration and exploitation. Optimization results on standard test suits indicate that SADE outperforms CDE in the global search ability

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006