Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO: A novel hybrid intelligent method

An effective hybrid optimization method is proposed by integrating an adaptive Kriging (A-Kriging) into an improved partial swarm optimization algorithm (IPSO) to give a so-called A-Kriging-IPSO for maxi-mizing the buckling load of laminated composite plates (LCPs) under uniaxial and biaxial compressions. In this method, a novel iterative adaptive Kriging model, which is structured using two training sample sets as active and adaptive points, is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process. The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples. The cell-based smoothed discrete shear gap method (CS-DSG3) is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets. The buckling load of the LCPs is maximized by utilizing the IPSO algorithm. To demonstrate the efficiency and accuracy of the proposed methodology, the LCPs with different layers (2, 3, 4, and 10 layers), boundary conditions, aspect ratios and load patterns (biaxial and uniaxial loads) are investigated. The results obtained by proposed method are in good agreement with the literature results, but with less computational burden. By applying adaptive radial Kriging model, the accurate optimal results-based predictions of the buckling load are obtained for the studied LCPs.     

Stochastically minimizing the makespan in flow shops

In this article, we are concerned with scheduling stochastic jobs in a flowshop with m machines and zero intermediate storage. We assume that there are n - 2 identically distributed and 2 fast stochastic jobs. Roughly, the main result states that the makespan is stochastically minimized by placing one of the fast jobs first and the other last.