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A pareto following variation operator for fast-converging multiobjective evolutionary algorithms

Published: 12 July 2008 Publication History

Abstract

One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been used to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this work, we introduce a simple approach that has comparatively smaller computational cost and we have developed this model as a variation operator that can be used in any kind of multiobjective optimizer. When designing this model, we have considered the whole search procedure as a dynamic system that takes available objective values in current front as input and generates approximated design variables for the next front as output. Initial simulation experiments have produced encouraging results in comparison to NSGA-II. Our motivation was to increase the speed of the hosting optimizer. We have compared the performance of the algorithm with respect to the total number of function evaluation and Hypervolume metric. This variation operator has worst case complexity of O(nkN3), where N is the population size, n and k is the number of design variables and objectives respectively.

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    cover image ACM Conferences
    GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
    July 2008
    1814 pages
    ISBN:9781605581309
    DOI:10.1145/1389095
    • Conference Chair:
    • Conor Ryan,
    • Editor:
    • Maarten Keijzer
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    Published: 12 July 2008

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    Author Tags

    1. dynamic system identification
    2. evolutionary multiobjective optimization
    3. function evaluation
    4. variation operator

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    • (2015)Comprehensive Survey of the Hybrid Evolutionary AlgorithmsResearch Methods10.4018/978-1-4666-7456-1.ch015(322-343)Online publication date: 2015
    • (2014)Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problemsSwarm and Evolutionary Computation10.1016/j.swevo.2013.08.00414(31-47)Online publication date: Feb-2014
    • (2013)Comprehensive Survey of the Hybrid Evolutionary AlgorithmsInternational Journal of Applied Evolutionary Computation10.4018/jaec.20130401014:2(1-19)Online publication date: 1-Apr-2013
    • (2013)Issues with performance measures for dynamic multi-objective optimisation2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)10.1109/CIDUE.2013.6595767(17-24)Online publication date: Apr-2013
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    • (2011)Multiobjective evolutionary algorithms: A survey of the state of the artSwarm and Evolutionary Computation10.1016/j.swevo.2011.03.0011:1(32-49)Online publication date: Mar-2011
    • (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
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