Research Repository

Black-box expensive multiobjective optimization with adaptive in-fill rules

Chen, Q and Long, B and Zhang, Q (2016) Black-box expensive multiobjective optimization with adaptive in-fill rules. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.


To deal with real-life black-box expensive multiobjective optimization problems, we investigated the application of an optimization framework expanded from MOEA/D-EGO. As MOEA/D-EGO, Gaussian process modeling techniques are used to subtittute the evaluation of the problem itself. Apart from the expected improvement (EI) in-fill rule in the original MOEA/D-EGO, we define a process that adaptively selects of in-fill rule in each iteration from seven different in-fill rules, including confidence limit of different probability (CLp), probability of improvement (PI), and EI. The initial probabilities of selecting a specific in-fill rule are derived from applying the algorithm on ZDT test suite. The practical problem set-up and optimization results and lesson learned in the process are reported.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 14 Dec 2016 09:40
Last Modified: 30 Jun 2021 10:15

Actions (login required)

View Item View Item