Sourcebook of parallel computing
Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization
ACM Transactions on Mathematical Software (TOMS)
Using the GA and TAO toolkits for solving large-scale optimization problems on parallel computers
ACM Transactions on Mathematical Software (TOMS)
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination
INFORMS Journal on Computing
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Sprouting search-an algorithmic framework for asynchronous parallel unconstrained optimization
Optimization Methods & Software
International Journal of High Performance Computing Applications
Parallel Stochastic Global Optimization Using Radial Basis Functions
INFORMS Journal on Computing
Computational intelligence methods for process control: fed-batch fermentation application
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Control of dead-time systems using derivative free particle swarm optimisation
International Journal of Bio-Inspired Computation
Joint segmentation and deformable registration of brain scans guided by a tumor growth model
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A parallel, asynchronous method for derivative-free nonlinear programs
ICMS'06 Proceedings of the Second international conference on Mathematical Software
Parallel optimization methods based on direct search
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Parallel tuning of support vector machine learning parameters for large and unbalanced data sets
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
A Low-rate Data Transfer Technique for Compressed Voice Channels
Journal of Signal Processing Systems
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We introduce a new asynchronous parallel pattern search (APPS). Parallel pattern search can be quite useful for engineering optimization problems characterized by a small number of variables (say, fifty or less) and by objective functions that are expensive to evaluate, such as those defined by complex simulations that can take anywhere from a few seconds to many hours to run. The target platforms for APPS are the loosely coupled parallel systems now widely available. We exploit the algorithmic characteristics of pattern search to design variants that dynamically initiate actions solely in response to messages, rather than routinely cycling through a fixed set of steps. This gives a versatile concurrent strategy that allows us to effectively balance the computational load across all available processors. Further, it allows us to incorporate a high degree of fault tolerance with almost no additional overhead. We demonstrate the effectiveness of a preliminary implementation of APPS on both standard test problems as well as some engineering optimization problems.