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A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

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Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2018). A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization. IEEE Transactions on Evolutionary Computation, 22(1), 129-142. https://doi.org/10.1109/TEVC.2016.2622301
Published in
IEEE Transactions on Evolutionary Computation
Authors
Chugh, Tinkle |
Jin, Yaochu |
Miettinen, Kaisa |
Hakanen, Jussi |
Sindhya, Karthik
Date
2018
Discipline
TietotekniikkaMathematical Information Technology
Copyright
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

 
We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm. ...
Publisher
Institute of Electrical and Electronics Engineers
ISSN Search the Publication Forum
1089-778X
Keywords
multiobjective optimization reference vectors surrogate-assisted evolutionary algorithms model management Kriging computational cost Pareto optimality Bayesian optimization monitavoiteoptimointi vektorit (matematiikka) algoritmit pareto-tehokkuus bayesilainen menetelmä päätöksenteko koneoppiminen
DOI
https://doi.org/10.1109/TEVC.2016.2622301
URI

http://urn.fi/URN:NBN:fi:jyu-201802061435

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/26550136

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