A self-adaptive combined strategies algorithm for constrained optimization using differential evolution
A self-adaptive combined strategies algorithm for constrained optimization using differential evolution
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DOI：
10.1016/j.amc.2014.05.018
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
S. Elsayed;R. Sarker;D. Essam
中科院分区：
文献类型：
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作者：
S. Elsayed;R. Sarker;D. Essam
在文献中已经提出了大量用于解决约束问题的差异进化变体。然而，没有一种方法被认为是解决具有不同数学性质的广泛问题的被广泛接受的方法。因此，为了更好地覆盖问题特征，本文引入了一种自适应差分进化算法。为此，它结合使用多个搜索操作符和多个约束处理技术。通过对一组众所周知的问题的实验分析，证明了这种方法的必要性。实验结果表明，该算法优于其他先进的算法。
There are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems. However, none of them was considered as being a well-accepted approach for solving a broad range of problems with different mathematical properties. Therefore, in this paper, for a better coverage of the problem characteristics, a self-adaptive differential evolution algorithm is introduced. To do that, it uses multiple search operators in conjunction with multiple constraint handling techniques. The need for such an approach is justified by experimental analysis on a well-known set of problems. The results show that the proposed algorithm is superior to other state-of-the-art algorithms.