Distributed Genetic Algorithm for Structural Optimization

Abstract

Parallel algorithms for optimization of structures reported in the literature have been restricted to shared-memory multiprocessors. This paper presents a distributed genetic algorithm for optimization of large structures on a cluster of workstations connected via a local area network (LAN). The selection of genetic algorithm is based on its adaptability to a high degree of parallelism. Two different approaches are used to transform the constrained structural optimization problem to an unconstrained optimization problem: a penalty-function method and augmented Lagrangian approach. For the solution of the resulting simultaneous linear equations the iterative preconditioned conjugate gradient (PCG) method is used because of its low memory requirement. A dynamic load-balancing mechanism is developed to account for the unpredictable multiuser, multasking environment of a networked cluster of workstations, heterogeneity of machines, and indeterminate nature of the interative PCG equation solver. The algorithm has been applied to optimization of a large space steel structure subjected to vertical and horizontal loads and the constraints of the AISC ASD specifications.