Distributed neural dynamics algorithms for optimization of large steel structures

Optimization of large structures consisting of thousands of members subjected to the highly nonlinear constraints of the actual commonly used design codes, such as the American Institute of Steel Construction (AISC), Allowable Stress Design (ASD), or Load and Resistance Factor Design (LRFD) specifications (AISC 1989, 1994), requires high-performance computing resources. We have previously developed parallel optimization algorithms on shared memory multiprocessors where a few powerful processors are connected to a single shared memory. In contrast, in a distributed memory machine, a relatively large number of microprocessors are connected to their own locally distributed memories without globally shared memory. In this article, we present distributed nonlinear neural dynamics algorithms for discrete optimization of large steel structures. The algorithms are implemented on a recently introduced distributed memory machine, the GRAY T3D, and applied to the minimum weight design of three large space steel structures ranging in size from 1,310 to 8,904 members. The stability, convergence, and efficiency of the algorithms are demonstrated through examples. For an 8,904-member structure, a high parallel processing efficiency of 94% is achieved using a 32-processor configuration.