Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times

Xilu Wang; Yaochu Jin; Sebastian SchmittMarkus Olhofer

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Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times

机译：Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times

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Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assumethat each objective function can be evaluated within the same period of time. Typically.this is untenable in many real-world optimization scenarios where evaluationof different objectives involves different computer simulations or physical experimentswith distinct time complexity. To address this issue, a transfer learning schemebased on surrogate-assisted evolutionary algorithms (SAEAs) is proposed, in which aco-surrogate is adopted tomodel the functional relationship between the fast and slowobjective functions and a transferable instance selection method is introduced to acquireuseful knowledge from the search process of the fast objective. Our experimentalresults on DTLZ and UF test suites demonstrate that the proposed algorithm is competitivefor solving bi-objective optimization where objectives have non-uniform evaluationtimes.

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《Evolutionary computation》 |2022年第2期|221-251|共31页
作者
Xilu Wang; Yaochu Jin; Sebastian SchmittMarkus Olhofer;
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作者单位

Department of Computer Science, University of Surrey, Guildford, GU2 7XH,United Kingdom;

Faculty of Technology, Bielefeld University, D-33615 Bielefeld, GermanyDepartment of Computer Science, University of Surrey,Guildford, GU2 7XH, United Kingdom;

Honda Research Institute Europe GmbH, Carl-Legien-Strasse 30,D-63073 Offenbach/Main, Germany;

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正文语种英语
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Multiobjective optimization; non-uniform evaluation times; transfer learning; cosurrogate; Gaussian process; surrogate-assisted evolutionary algorithm; Bayesian optimization;

Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times

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