Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

17 Scopus Citations
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  • Zhenkun Wang
  • Yew-Soon Ong
  • Shunyu Yao
  • Haitao Liu
  • Jianping Luo


Original languageEnglish
Journal / PublicationIEEE Transactions on Cybernetics
Online published24 Nov 2021
Publication statusOnline published - 24 Nov 2021


In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multiple subproblems. However, these subproblems may be unnecessary or resolved. Operating on such subproblems can cause server inefficiencies, especially in the case of expensive optimization. To overcome this shortcoming, we propose an adaptive subproblem selection (ASS) strategy to identify the most promising subproblems for further modeling. To better leverage the cross information between the subproblems, we use the collaborative multioutput Gaussian process surrogate to model them jointly. Moreover, the commonly used acquisition functions (also known as infill criteria) are investigated in this article. Our analysis reveals that these acquisition functions may cause severe imbalances between exploitation and exploration in multiobjective optimization scenarios. Consequently, we develop a new acquisition function, namely, adaptive lower confidence bound (ALCB), to cope with it. The experimental results on three different sets of benchmark problems indicate that our proposed algorithm is competitive. Beyond that, we also quantitatively validate the effectiveness of the ASS strategy, the CoMOGP model, and the ALCB acquisition function.

Research Area(s)

  • Optimization, Predictive models, Computational modeling, Training, Linear programming, Gaussian processes, Computer science, Adaptive lower confidence bound (ALCB), expensive optimization, multiobjective optimization, multioutput Gaussian process (GP), subproblem selection, EVOLUTIONARY ALGORITHMS, FITNESS APPROXIMATION, GAUSSIAN-PROCESSES, SURROGATE MODEL, MOEA/D