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Hybridizing surrogate techniques, rough sets and evolutionary algorithms to efficiently solve multi-objective optimization problems

Authors:

Luis V. Santana-Quintero,

Carlos A. Coello Coello,

Alfredo G. Hernandez-DiazAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 763 - 764

https://doi.org/10.1145/1389095.1389244

Published: 12 July 2008 Publication History

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Abstract

This paper presents an approach in which a multi-objective evolutionary algorithm (MOEA) is coupled to a surrogate method in order to explore the search space in an efficient manner. A small comparative study among three surrogate methods is conducted: an artificial neural network (ANN), a radial basis function (RBF) and a support vector machine (SVM). The winner in this comparative study was the SVM. However, our results indicated that the spread of solutions achieved by our surrogate-based MOEA was poor. Thus, we decided to introduce a second phase to the algorithm in which it is hybridized with the rough sets in order to improve the spread of solutions and help to reach the true Pareto front. We show that our proposed hybrid approach only requires 2,000 fitness function evaluations in order to solve test problems with up to 30 decision variables.

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Coello Coello C(2014)Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current ChallengesAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences10.1007/978-3-319-11541-2_1(3-18)Online publication date: 15-Nov-2014
https://doi.org/10.1007/978-3-319-11541-2_1
Rosales-Perez ACoello CGonzalez JReyes-Garcia CEscalante H(2013)A hybrid surrogate-based approach for evolutionary multi-objective optimization2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557876(2548-2555)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557876
Montemayor-Garcia GToscano-Pulido G(2011)A study of surrogate models for their use in multiobjective evolutionary algorithms2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control10.1109/ICEEE.2011.6106655(1-6)Online publication date: Oct-2011
https://doi.org/10.1109/ICEEE.2011.6106655
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Index Terms

Hybridizing surrogate techniques, rough sets and evolutionary algorithms to efficiently solve multi-objective optimization problems
1. Mathematics of computing
  1. Mathematical analysis
    1. Functional analysis
      1. Approximation
    2. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Mathematical optimization

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Published In

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
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Coello Coello C(2014)Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current ChallengesAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences10.1007/978-3-319-11541-2_1(3-18)Online publication date: 15-Nov-2014
https://doi.org/10.1007/978-3-319-11541-2_1
Rosales-Perez ACoello CGonzalez JReyes-Garcia CEscalante H(2013)A hybrid surrogate-based approach for evolutionary multi-objective optimization2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557876(2548-2555)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557876
Montemayor-Garcia GToscano-Pulido G(2011)A study of surrogate models for their use in multiobjective evolutionary algorithms2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control10.1109/ICEEE.2011.6106655(1-6)Online publication date: Oct-2011
https://doi.org/10.1109/ICEEE.2011.6106655
López PRivera APérez-Godoy MJesus MCarmona C(2009)EMORBFNProceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence10.1007/978-3-642-02478-8_94(752-759)Online publication date: 5-Jun-2009
https://dl.acm.org/doi/10.1007/978-3-642-02478-8_94

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Hybridizing an evolutionary algorithm with mathematical programming techniques for multi-objective optimization

Alternative techniques to solve hard multi-objective optimization problems

Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

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