在渐进结构优化设计的各种优化判据下都存在这一现象。

This phenomenon is all existed under various kinds of optimization criterion of the progressive structural optimization design.

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该算法直接在基因的层面上进行优化，能学习劣解的基因，并用信息熵作为结束条件的判据。

GOA optimizes directly at the gene level and can learn from the gene of bad individuals. The entropy is used for the terminal criterion of the algorithm.

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结构表明，SNTO法是一种实用的全局优化方法，收缩比和收敛判据的改进可大大缩短计算时间。

The result shows that SNTO is a global optimization method and it can save calculation time with such a modification.

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