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GECCO
2007
Springer

A NSGA-II, web-enabled, parallel optimization framework for NLP and MINLP

9 years 4 months ago
A NSGA-II, web-enabled, parallel optimization framework for NLP and MINLP
Engineering design increasingly uses computer simulation models coupled with optimization algorithms to find the best design that meets the customer constraints within a time constrained deadline. The continued application of Moore’s law combined with linear speedups of coarse grained parallelization will allow more designs to be evaluated in shorter periods of time. This paper presents a scalable, standards based framework that uses web services and grid services with a multiple objective genetic algorithm to solve continuous, mixed integer, single objective or multiple objective nonlinear, constrained design problems. Test data is provided to validate a linear speedup based on the number of processors and to show the robustness of the genetic algorithm on a set of 10 design problems. Categories and Subject Descriptors D.3.3 [Programming Languages]: Language Constructs and Features—Frameworks, concurrent programming structures; G.4 [Mathematical Software]: User Interfaces; I.6 [...
David J. Powell, Joel K. Hollingsworth
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors David J. Powell, Joel K. Hollingsworth
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