Modified evolutionary algorithm for global optimization

被引:3
作者
Guo Chonghui1
2. Department of Applied Mathematics
机构
关键词
global optimization; evolutionary algorithms; chaos search;
D O I
暂无
中图分类号
TP301.6 [算法理论];
学科分类号
081202 ;
摘要
A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase I. Adjustment strategy of steplength and intensive searches in Phase II are employed. The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
引用
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页码:1 / 6
页数:6
相关论文
共 1 条
[1]   NEURAL COMPUTATION OF DECISIONS IN OPTIMIZATION PROBLEMS [J].
HOPFIELD, JJ ;
TANK, DW .
BIOLOGICAL CYBERNETICS, 1985, 52 (03) :141-152