A study of parallel evolution strategy: pattern search on a GPU computing platform
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Multi-walk Parallel Pattern Search Approach on a GPU Computing Platform
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Leveraging efficient parallel pattern search for clock mesh optimization
Proceedings of the 2009 International Conference on Computer-Aided Design
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
A parallel, asynchronous method for derivative-free nonlinear programs
ICMS'06 Proceedings of the Second international conference on Mathematical Software
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Simultaneous optimization and uncertainty quantification
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
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We present a new asynchronous parallel pattern search (APPS) method which is different from that developed previously by Hough, Kolda, and Torczon. APPS efficiently uses parallel and distributed computing platforms to solve science and engineering design optimization problems where derivatives are unavailable and cannot be approximated. The original APPS was designed to be fault-tolerant as well as asynchronous and was based on a peer-to-peer design. Each process was in charge of a single, fixed search direction. Our new version is based instead on a manager-worker paradigm. Though less fault-tolerant, the resulting algorithm is more flexible in its use of distributed computing resources. We further describe how to incorporate a zero-order sufficient decrease condition and handle bound constraints. Convergence theory for all situations (unconstrained and bound constrained as well as simple and sufficient decrease) is developed. We close with a discussion of how the new APPS will better facilitate the future incorporation of linear and nonlinear constraints.