Interactive decomposition multiobjective optimization via progressively learned value functions

Ke LI, Renzhi CHEN, Dragan SAVIC, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

30 Citations (Scopus)


Decomposition has become an increasingly popular technique for evolutionary multiobjective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, a decision maker (DM) might only be concerned in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even noisy to the decision-making procedure. Furthermore, there is no guarantee that the preferred solutions will be found when many-objective problems. This paper develops an interactive framework for the decomposition-based EMO algorithm to lead a DM to the preferred solutions of her/his choice. It consists of three modules, i.e., consultation, preference elicitation, and optimization. Specifically, after every several generations, the DM is asked to score a few candidate solutions in a consultation session. Thereafter, an approximated value function, which models the DM's preference information, is progressively learned from the DM's behavior. In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i.e., a set of reference points that are biased toward the ROI. The optimization module, which can be any decomposition-based EMO algorithm in principle, utilizes the biased reference points to guide its search process. Extensive experiments on benchmark problems with three to ten objectives fully demonstrate the effectiveness of our proposed method for finding the DM's preferred solutions. © 1993-2012 IEEE.
Original languageEnglish
Article number8531708
Pages (from-to)849-860
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Issue number5
Early online date12 Nov 2018
Publication statusPublished - May 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the Ministry of ary multiobjective optimization (EMO) algorithms, such as non-Science and Technology of China under Grant 2017YFC0804003, in part by dominated sorting genetic algorithm II (NSGA-II) [7], indicator-and Technology Innovation Committee Foundation of Shenzhen under Grant the Royal Society under Grant IEC/NSFC/170243, in part by the Science based EA (IBEA) [8] and multiobjective EA based on decom-ZDSYS201703031748284, in part by the Shenzhen Peacock Plan under Grant position (MOEA/D) [9], to find a set of well-converged and KQTD2016112514355531,and in part by the Engineering and PhysicalSci- well-diversified efficient solutions that approximate the whole (Ke Li and Renzhi Chen contributed equally to this work.)


  • Decomposition-based technique
  • evolutionary computation
  • interactive multiobjective optimization (MOP)
  • multicriterion decision making (MCDM)


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