Abstract
In this paper, an efficient optimization method named NIO-SLP is suggested for uncertain structures by using the non-probabilistic interval model to quantify the uncertainty. A general nonlinear interval optimization (NIO) problem is investigated, in which both of the objective function and constraints are nonlinear and uncertain. A sequence of approximate optimization sub-problems are created based on the first-order Taylor expansion and each one degenerates into a simple linear interval optimization (LIO) problem. An iterative mechanism is proposed to update the design space and whereby make the optimization sequence converge at the optimum. Two numerical examples are investigated to demonstrate the effectiveness of the present method.
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