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Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization
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AI
DOI：10.1109/TCYB.2021.3126341.png)
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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.
Keyword:
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
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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