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On the Efficiency of a Global Non-differentiable Optimization Algorithm Based on the Method of Optimal Set Partitioning

机译:基于最优集划分方法的全局不可微优化算法的效率

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摘要

The examined algorithm for global optimization of the multiextremal non-differentiable function is based on the following idea: the problem of determination of the global minimum point of the function f(x) on the set Ω(f(x) has a finite number of local minima in this domain) is reduced to the problem of finding all local minima and their attraction spheres with a consequent choice of the global minimum point among them. This reduction is made by application of the optimal set partitioning method. The proposed algorithm is evaluated on a set of well-known one-dimensional, two-dimensional and three-dimensional test functions. Recommendations for choosing the algorithm parameters are given.
机译:检验的用于极值不可微函数的全局最优化的算法基于以下思想:确定集合Ω(f(x)上的函数f(x)的全局最小点的问题具有有限个该域中的局部极小值)简化为寻找所有局部极小值及其吸引球的问题,并因此在其中选择了全局最小点。这种减少是通过应用最佳集划分方法来实现的。该算法在一组著名的一维,二维和三维测试函数上进行了评估。给出了选择算法参数的建议。

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