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Non-uniform mode-pursuing sampling method based on multivariate multimodal distribution model

机译:基于多元多峰分布模型的非均匀求模采样方法

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A non-uniform distributing based mode-pursuing sampling method on black-box problem is proposed for the computation-intensive global optimization problem. The multivariate multimodal distribution model is established based on the expensive sample points of original optimization problem, which is controlled by the convergence principle of multiple correlation coefficient through variance of probability distribution. More design points are generated progressively around the current optimal regions and constitute the non-uniform discrete design space. Non-uniform sampling strategy changes the uniform distributing characteristic of design space, improves the utilization efficiency of design points and strengthens the distribution rationality of discrete design space. Analytical and numerical test results show that the improved method is more efficient and accurate than standard mode-pursuing sampling method and traditional algorithms, and has broad prospects for the expensive black-box problem.
机译:针对计算量大的全局最优化问题,提出了一种基于非均匀分布的黑盒模式寻踪抽样方法。基于原始最优化问题的昂贵样本点建立了多元多峰分布模型,该模型通过概率分布的方差的多重相关系数的收敛原理来控制。在当前的最佳区域附近逐渐生成更多的设计点,并构成不均匀的离散设计空间。非均匀采样策略改变了设计空间的均匀分布特性,提高了设计点的利用效率,增强了离散设计空间的分布合理性。分析和数值测试结果表明,改进的方法比标准的模式采样方法和传统算法更有效,更准确,对于昂贵的黑箱问题具有广阔的前景。

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