Optimization of space structures by neural dynamics

Abstract

A nonlinear neural dynamics model is presented as a new structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. A pseudo-objective function is formulated for the optimization problem in the form of a Lyapunov function to ensure the global convergence and the stability of the neural dynamic system by adopting an exterior penalty function method. The topology of the neural dynamics model consists of one variable layer and multi-constraint layers. The number of constraint layers corresponds to the number of loading conditions in the structural optimization problem. Design sensitivity coefficients calculated by the adjoint variable method are included in the inhibitory connections from the constraint layers to the variable layer. Optimum weights and design solutions are presented for four example structures and compared with those reported in the literature.