GP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models

Miha Mlakar, Dejan Petelin, Tea Tušar and Bogdan Filipič

European Journal of Operational Research, 2015, vol. 243, issue 2, 347-361

Abstract: This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization based on Gaussian Process models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly performed comparisons of solutions due to inaccurate surrogate model approximations. The GP-DEMO algorithm was tested on several benchmark problems and two computationally expensive real-world problems. To be able to assess the results we compared them with another surrogate-model-based algorithm called Generational Evolution Control (GEC) and with the Differential Evolution for Multiobjective Optimization (DEMO). The quality of the results obtained with GP-DEMO was similar to the results obtained with DEMO, but with significantly fewer exactly evaluated solutions during the optimization process. The quality of the results obtained with GEC was lower compared to the quality gained with GP-DEMO and DEMO, mainly due to wrongly performed comparisons of the inaccurately approximated solutions.

Keywords: Multiple objective programming; Evolutionary algorithms; Surrogate models; Gaussian Process modeling; Probable Pareto dominance (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:243:y:2015:i:2:p:347-361

DOI: 10.1016/j.ejor.2014.04.011

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