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An informed convergence accelerator for evolutionary multiobjective optimiser

Published: 07 July 2007 Publication History

Abstract

A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through the use of 'mirage' solutions is introduced and investigated. The accelerator is meant to be a portable component that can be plugged into any stochastic optimisation algorithm, such as genetic algorithms. The purpose of the new component termed as the Informed Convergence Accelerator (ICA) is to enhance the search capability, convergence extent and most especially the speed of convergence of the hosting stochastic global optimisation technique. ICA was hybridized with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Enhanced results were achieved demonstrating the utility of the introduced component.

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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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Published: 07 July 2007

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Author Tags

  1. convergence acceleration
  2. evolutionary multiobjective optimisation

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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

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  • (2018)On Surrogate-Based Optimization of Truly Reversible Blade Profiles for Axial FansDesigns10.3390/designs20200192:2(19)Online publication date: 20-Jun-2018
  • (2016)A Review of Surrogate Assisted Multiobjective Evolutionary AlgorithmsComputational Intelligence and Neuroscience10.1155/2016/94204602016(19)Online publication date: 1-Jun-2016
  • (2014)A new multi-objective approach to finite element model updatingJournal of Sound and Vibration10.1016/j.jsv.2014.01.015333:11(2323-2338)Online publication date: May-2014
  • (2012)Multi-objective differential evolution with self-navigation2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/ICSMC.2012.6377775(508-513)Online publication date: Oct-2012
  • (2009)The Pareto-following variation operator as an alternative approximation modelProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689601(8-15)Online publication date: 18-May-2009
  • (2009)The Pareto-Following Variation Operator as an alternative approximation model2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982924(8-15)Online publication date: May-2009
  • (2009)A Convergence Acceleration Technique for Multiobjective OptimisationMulti-Objective Memetic Algorithms10.1007/978-3-540-88051-6_9(183-205)Online publication date: 2009
  • (2008)A pareto following variation operator for fast-converging multiobjective evolutionary algorithmsProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389234(721-728)Online publication date: 13-Jul-2008
  • (2008)Towards high speed multiobjective evolutionary optimizersProceedings of the 10th annual conference companion on Genetic and evolutionary computation10.1145/1388969.1388972(1791-1794)Online publication date: 12-Jul-2008
  • (2008)A Pareto following variation operator for evolutionary dynamic multi-objective optimization2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)10.1109/CEC.2008.4631100(2270-2277)Online publication date: Jun-2008

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