A1 Journal article (refereed)
A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization (2019)
Habib, A., Singh, H. K., Chugh, T., Ray, T., & Miettinen, K. (2019). A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 23(6), 1000-1014. https://doi.org/10.1109/TEVC.2019.2899030
JYU authors or editors
Publication details
All authors or editors: Habib, Ahsanul; Singh, Hemant Kumar; Chugh, Tinkle; Ray, Tapabrata; Miettinen, Kaisa
Journal or series: IEEE Transactions on Evolutionary Computation
ISSN: 1089-778X
eISSN: 1941-0026
Publication year: 2019
Volume: 23
Issue number: 6
Pages range: 1000-1014
Publisher: Institute of Electrical and Electronics Engineers
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/TEVC.2019.2899030
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/66638
Publication is parallel published: https://ore.exeter.ac.uk/repository/handle/10871/35930
Abstract
Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve them. However, computationally expensive MaOPs have been scarcely investigated. Typically, surrogate-assisted methods have been used in the literature to tackle computationally expensive problems, but such studies have largely focused on problems with 1–3 objectives. In this paper, we present an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs. The key features of the approach include: 1) the use of multiple surrogates to effectively approximate a wide range of objective functions; 2) use of two sets of reference vectors for improved performance on irregular Pareto fronts (PFs); 3) effective use of archive solutions during offspring generation; and 4) a local improvement scheme for generating high quality infill solutions. Furthermore, the approach includes constraint handling which is often overlooked in contemporary algorithms. The performance of the approach is benchmarked extensively on a set of unconstrained and constrained problems with regular and irregular PFs. A statistical comparison with the existing techniques highlights the efficacy and potential of the approach.
Keywords: multi-objective optimisation; evolutionary computation
Free keywords: multiprotocol label switching; multiobjective optimization; metamodels; reference vectors; computational cost
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2019
JUFO rating: 3