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

Published: 07 July 2007 Publication History

Abstract

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.

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Cited By

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  • (2023)Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal PerspectiveComputational Intelligence10.1007/978-3-031-46221-4_5(90-120)Online publication date: 3-Nov-2023
  • (2019)Evolutionary multiobjective optimization: open research areas and some challenges lying aheadComplex & Intelligent Systems10.1007/s40747-019-0113-46:2(221-236)Online publication date: 19-Jun-2019
  • (2018)Parallel predator---prey interaction for evolutionary multi-objective optimizationNatural Computing: an international journal10.1007/s11047-011-9266-911:3(519-533)Online publication date: 20-Dec-2018
  • Show More Cited By

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Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. design patterns
  2. grid engine
  3. multiple objective genetic algorithm
  4. nonlinear constrained optimization
  5. service oriented architecture
  6. web services

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal PerspectiveComputational Intelligence10.1007/978-3-031-46221-4_5(90-120)Online publication date: 3-Nov-2023
  • (2019)Evolutionary multiobjective optimization: open research areas and some challenges lying aheadComplex & Intelligent Systems10.1007/s40747-019-0113-46:2(221-236)Online publication date: 19-Jun-2019
  • (2018)Parallel predator---prey interaction for evolutionary multi-objective optimizationNatural Computing: an international journal10.1007/s11047-011-9266-911:3(519-533)Online publication date: 20-Dec-2018
  • (2017)MIDACO Parallelization Scalability on 200 MINLP BenchmarksJournal of Artificial Intelligence and Soft Computing Research10.1515/jaiscr-2017-00127:3(171-181)Online publication date: 20-Mar-2017
  • (2016)Numerical assessment of the parallelization scalability on 200 MINLP benchmarks2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743877(830-837)Online publication date: Jul-2016
  • (2013)Parallelization strategies for evolutionary algorithms for MINLP2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557628(635-641)Online publication date: Jun-2013

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