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Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

Wang, Handing, Jin, Yaochu, Sun, Chaoli and Doherty, John (2018) Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles IEEE Transactions on Evolutionary Computation.

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Abstract

In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Wang, Handinghanding.wang@surrey.ac.uk
Jin, YaochuYaochu.Jin@surrey.ac.uk
Sun, Chaoli
Doherty, Johnjohn.doherty@surrey.ac.uk
Date : 10 May 2018
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/TEVC.2018.2834881
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Uncontrolled Keywords : Offline data-driven optimization; Surrogate; Evolutionary algorithm; Ensemble; Radial basis function networks
Related URLs :
Depositing User : Clive Harris
Date Deposited : 08 May 2018 14:12
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/846370

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