Enhanced self-adaptive evolutionary algorithm for numerical optimization

被引:0
|
作者
Yu Xue 1
2. No.723 Institute of China Shipbuilding Industry Corporation
3. Science and Technology on Electron-optic Control Laboratory
机构
关键词
self-adaptive; numerical optimization; evolutionary algorithm; stochastic search algorithm;
D O I
暂无
中图分类号
TP301.6 [算法理论]; O224 [最优化的数学理论];
学科分类号
070105 ; 081202 ; 1201 ;
摘要
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
引用
收藏
页码:921 / 928
页数:8
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