Programming python
NetSolve: a network server for solving computational science problems
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Learning Python
Genetic Algorithms
Asynchronous Parallel Pattern Search for Nonlinear Optimization
SIAM Journal on Scientific Computing
A Scalable Approach to Network Enabled Servers (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Solving simulation optimization problems on grid computing systems
Parallel Computing - Optimization on grids - Optimization for grids
Particle swarm optimization for integer programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Future Generation Computer Systems
Enabling applications for grid computing with globus
Enabling applications for grid computing with globus
Computers and Industrial Engineering
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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.