Simpat: Self-Bounding Direct Search Method for Optimization
Simpat: Self-Bounding Direct Search Method for Optimization
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DOI：
10.1021/i260045a018
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发表时间：
1973
期刊：
影响因子：
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通讯作者：
D. Keefer
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文献类型：
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作者：
D. Keefer
单纯形搜索方法已被修改，包括一个新的程序，用于处理独立变量的界限。通过对独立变量集的递归划分，模式搜索与Simplexsearch合并，形成一个名为Applaat的复合爬山器。模式搜索用于那些处于或非常接近其边界的变量，而单纯形方法应用于其余变量。因此，搜索是自约束的，并且是一种直接搜索方法，因为它只需要目标函数的值就可以继续进行。测试问题的结果最令人鼓舞。结合处理约束条件的罚函数策略，Quarterat在优化现实工程经济模型方面取得了巨大的成功，包括几个非常大和复杂的模型。
The Simplex search method has been modified to include a new procedure for dealing with bounds on the independent variables. Throughrecursive partitioning of the independent variable set, Pattern search is merged with Simplexsearch to form a composite hillclimber named Simpat. In Simpat, Pattern search is used for those variables which are at or are very near their bounds, while the Simplex method is applied to the remaining variables. Consequently, Simpat is self-bounding and is a direct search method in that it requires only values for the objective function in order to proceed. Results on test problems have been most en-couraging. In conjunction with a penalty function strategy for handling constraints, Simpat has proved ex-tremely successful in optimizing realistic engineering-economic models—including severalwhich were ex-ceedingly large and complex.