Hybrid Optimization Schemes for Global Optimization: Wing Modeling of Micro-Aerial Vehicles - Velazquez, Leticia; Argaez, Miguel; Sanchez, R.; Ramirez, C.; Hernandez, Miguel; Culbreth, M.; Jameson, A. (2010 DoD High Performance Computing Modernization Program Users Group Conference, 2010)
In this paper, we present a parallel hybrid algorithm for solving global optimization problems that is based on the coupling of a stochastic global (Simultaneous-Perturbation Stochastic Approximation, Simulated Annealing, Genetic Algorithms) and a local method (Newton-Krylov Interior-Point) via a surrogate model. There exist several algorithms for finding approximate global solutions, but our technique will further guarantee that such solutions satisfy physical bounds of the problem. First, the Simultaneous-Perturbation Stochastic Approximation (SPSA) algorithm conjectures regions where a global solution may exist. Next, some data points from the regions are selected to generate a continuously differentiable surrogate model that approximates the original function. Finally, the Newton-Krylov Interior-Point (NKIP) algorithm is applied to the surrogate model subject to bound constraints for obtaining a feasible approximate global solution. The hybrid optimization code is being applied to Stanford's UFLO Computational Fluid Dynamics (CFD) code. This code is used by the US Army High Performance Computing Research Center (AHPCRC) to develop flapping- and twisting-wing models for Micro Aerial Vehicles (MAV), hummingbird-sized airborne vehicles that can be used for sensing and surveillance. We present some preliminary numerical results of the large-scale high performance computing (HPC) hybrid optimization C code that is being run on the Department of Defense MANA machine from Maui, Hawaii.