An adaptive multi-scale conjugate gradient method for distributed parameter estimations (or inverse problems) of wave equation is presented. The identification of the coefficients of wave equations in two dimensions is considered. First, the conjugate gradient method for optimization is adopted to solve the inverse problems. Second, the idea of multi-scale inversion and the necessary conditions...

Point cloud filtering is an important prerequisite for three-dimensional surface modeling with high precision based on LiDAR data. To cope the issues of low accuracy or excessive model complexity in traditional algorithms, this paper proposes a method point multi-scale convolutional neural network incorporated attention mechanism. Firstly, regular image patch centering each constructed elevatio...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...

    Supplier selection,   Multi-objective decision making,   Fuzzy Compromise programming,   Supply chain management,   Quantity discount .   Supplier selection is naturally a complex multi-objective problem including both quantitative and qualitative factors. This paper deals with this issue from a new view point. A quantity discount situation, which plays a role of motivator for buyer, is con...

Multi-objective optimization problems are often accompanied by complex black-box functions which not only increases the difficulty of solving, but also solving time. In order to reduce computational cost such multi-objective problems, this paper proposes an ARBF-MLPA (Adaptive Radial Basis Function neural network combined with Machine Learning Point Adding) method, uses ABRF Function) and OLHS ...