latin hypercube sampling followed optimization

Y., 2006, 'Shape optimization of three-dimensional channel roughened by angled ribs with RANS analysis of turbulent heat transfer,' International Journal of Heat and Mass Transfer, Vol. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems.

The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). The important findings are illustrated using Box-plots. The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space.

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model.

Latin hypercube sampling followed optimization