A DIFFERENTIAL EVOLUTION WITH SIMULATED ANNEALING UPDATING METHOD
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.
Differential evolution simulated annealing function optimization Global search
JING-YU YAN QING LING DE-MIN SUN
Automation Department, University of Science and Technology of China, Hefei 230027, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2103-2106
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）