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Inspired by the so-called "divide-and-conquer" principle that is often used to attack a complex problem by dividing it into simpler problems, a three-stage SVM ensemble algorithm is proposed to improve its prediction accuracy and generalization performance for chaotic time series. In the first stage, Fuzzy C-means clustering algorithm is adopted to(More)
A clustering based composite kernels support vector machine ensemble forecasting model is proposed for the chaotic time series prediction. First, fuzzy possibility c-mean clustering algorithm (FPCM) is adopted to partition the input dataset into several subsets, which can overcome the drawback caused by outlier and noise in conventional fuzzy c-mean method.(More)
The three-dimensional bin packing problem consists of packing a set of boxes into the minimum number of bins. This paper presents a new GRASP algorithm for solving three-dimensional bin packing problems. More precisely, the constructive phase is based on a caving degree (CD) heuristic developed for the container loading problem. In the improvement phase,(More)
This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of immune particle swarm optimization (IPSO) and Integer Linear Programming (ILP) model. More precisely, an IPSO engine works as a generator of reduced instances for the original CL problem, which are formulated as ILP(More)
This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of caving degree (CD) algorithm and variable neighborhood descent (VND) algorithm. More precisely, the first constructive phase is based on a caving degree (CD) heuristic developed for the single container loading problem. In the second(More)
A soft sensor was developed to estimate the concentrate grade and recovery rate of a flotation circuit. The algorithm uses kernel principal component analysis (KPCA) and composite kernel support vector regression (CK-SVR) to perform the estimation. Firstly, the flotation prior knowledge and KPCA are employed to reduce the dimension of input vector of(More)
This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of Immune Genetic Algorithm (IGA) and Integer Linear Programming (ILP) model. More precisely, an IGA engine works as a generator of reduced instances for the original CL problem, which are formulated as ILP models. These instances, in turn,(More)
This An accurate prediction of important production indexes such as recovery rate and concentrate grade is essential for successful monitoring and controlling of a flotation circuit. However, due to the inherent characteristics of a flotation circuit such as time-delay, strong nonlinearities and uncertainties, these production indices are usually difficult(More)
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