Optimization With High-Cost Objective Function Evaluations in a Computing Grid and an Application To Simulation-Based Design

The emerging grid computing technologies are aimed at the creation of virtual supercomputers to conduct huge scale scientific computations by harvesting computing resources on the Internet. This paper introduces a grid-enabled implementation of an optimization program for large-scale optimization problems requiring high-cost, black-box objective function evaluations. Adopting grid computing can be particularly beneficial for building surrogates such as response surfaces and carrying out large-scale optimizations using stochastic optimization algorithms. However, several problems have to be resolved for effective utilization of grid resources because of heterogeneity in computer capacity among grid resources and limited network conditions inherent in grid systems. This paper identifies some of the problems and introduces algorithms to effectively carry out large-scale optimizations on a grid. Specifically, asynchronous genetic and particle swarm optimization algorithms are developed for grid computing environments. The performance and characteristics of the grid-enabled implementations are assessed via extensive numerical tests. Finally, structural design based on high-fidelity simulations is carried out using the proposed algorithm in a computing grid system.