Browsing by Author "Venter, Gerhard"
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- ItemAn algorithm for fast optimal Latin hypercube design of experiments(Wiley-Blackwell, 2010-04) Viana, Felipe A. C.; Venter, Gerhard; Balabanov, VladimirThis paper presents the translational propagation algorithm, a new method for obtaining optimal or near optimal Latin hypercube designs (LHDs) without using formal optimization. The procedure requires minimal computational effort with results virtually provided in real time. The algorithm exploits patterns of point locations for optimal LHDs based on the ɸp criterion (a variation of the maximum distance criterion). Small building blocks, consisting of one or more points each, are used to recreate these patterns by simple translation in the hyperspace. Monte Carlo simulations were used to evaluate the performance of the new algorithm for different design configurations where both the dimensionality and the point density were studied. The proposed algorithm was also compared against three formal optimization approaches (namely random search, genetic algorithm, and enhanced stochastic evolutionary algorithm). It was found that (i) the distribution of the ɸp values tends to lower values as the dimensionality is increased and (ii) the proposed translational propagation algorithm represents a computationally attractive strategy to obtain near optimum LHDs up to medium dimensions.
- ItemConstrained particle swarm optimization using a bi-objective formulation(Springer Verlag, 2010) Venter, Gerhard; Haftka, R. T.This paper introduces an approach for dealing with constraints when using particle swarm optimization. The constrained, single objective optimization problem is converted into an unconstrained, bi-objective optimization problem that is solved using a multi-objective implementation of the particle swarm optimization algorithm. A specialized bi-objective particle swarm optimization algorithm is presented and an engineering example problem is used to illustrate the performance of the algorithm. An additional set of 13 test problems from the literature is used to further validate the performance of the newly proposed algorithm. For the example problems considered here, the proposed algorithm produced promising results, indicating that it is an approach that deserves further consideration. The newly proposed algorithm provides performance similar to that of a tuned penalty function approach, without having to tune any penalty parameters.
- ItemNumerical optimisation of a small-scale wind turbine through the use of surrogate modelling(University of Cape Town, Energy Research Centre, 2017) Erfort, Gareth; Von Backstrom, Theodor Willem; Venter, GerhardENGLISH ABSTRACT: Wind conditions in South Africa are suitable for small-scale wind turbines, with wind speeds below 7 m.s−1. This investigation is about a methodology to optimise a full wind turbine using a surrogate model. A previously optimised turbine was further optimised over a range of wind speeds in terms of a new parameterisation methodology for the aerodynamic profile of the turbine blades, using non-uniform rational B-splines to encompass a wide range of possible shapes. The optimisation process used a genetic algorithm to evaluate an input vector of 61 variables, which fully described the geometry, wind conditions and rotational speed of the turbine. The optimal performance was assessed according to a weighted coefficient of power, which rated the turbine blade’s ability to extract power from the available wind stream. This methodology was validated using XFOIL to assess the final solution. The results showed that the surrogate model was successful in providing an optimised solution and, with further refinement, could increase the coefficient of power obtained.
- ItemUsing a filter-based SQP algorithm in a parallel environment(American Institute of Aeronautics and Astronautics, 2009-12) Venter, Gerhard; Vanderplaats, Garret N.A parallel, filter-based, sequential quadratic programming (SQP) algorithm is implemented and tested for typical general-purpose engineering applications. Constrained engineering test problems, including a finite element simulation, with up to 512 design variables are considered. The accuracy and serial performance of the filter-based algorithm are compared against that of a standard SQP algorithm. The parallel performance of the algorithm is evaluated, using up to 52 cores on a Linux Cluster. The results indicate that the filter-based algorithm competes favorably with a standard SQP algorithm in a serial environment. However, the filter-based algorithm exhibits much better parallel efficiency due to the lack of a one dimensional search.