Abstract: In the present study the efficiency of Big Bang-Big
Crunch (BB-BC) algorithm is investigated in discrete structural
design optimization. It is shown that a standard version of the BB-BC
algorithm is sometimes unable to produce reasonable solutions to
problems from discrete structural design optimization. Two
reformulations of the algorithm, which are referred to as modified
BB-BC (MBB-BC) and exponential BB-BC (EBB-BC), are
introduced to enhance the capability of the standard algorithm in
locating good solutions for steel truss and frame type structures,
respectively. The performances of the proposed algorithms are
experimented and compared to its standard version as well as some
other algorithms over several practical design examples. In these
examples, steel structures are sized for minimum weight subject to
stress, stability and displacement limitations according to the
provisions of AISC-ASD.
Abstract: There are a number of different cars for transferring hundreds of close contacts of swine influenza patients to hospital, and we need to carefully assign the passengers to those cars in order to minimize the risk of influenza spreading during transportation. The paper presents an approach to straightforward obtain the optimal solution of the relaxed problems, and develops two iterative improvement algorithms to effectively tackle the general problem.
Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.