期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 2, 页码 536-549出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2869674
关键词
Optimization; Radio frequency; Computational modeling; Vegetation; Forestry; Training; Linear programming; Constrained multiobjective combinatorial optimization; data-driven optimization; evolutionary algorithm (EA); radial basis function (RBF) networks; random forest (RF); surrogate; trauma systems
类别
资金
- EPSRC [EP/M017869/1]
- National Natural Science Foundation of China [61590922, 61876123]
- EPSRC [EP/M017869/1] Funding Source: UKRI
Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this paper, we address a class of expensive data-driven constrained multiobjective combinatorial optimization problems, where the objectives and constraints can be calculated only on the basis of a large amount of data. To solve this class of problems, we propose using random forests (RFs) and radial basis function networks as surrogates to approximate both objective and constraint functions. In addition, logistic regression models are introduced to rectify the surrogate-assisted fitness evaluations and a stochastic ranking selection is adopted to further reduce the influences of the approximated constraint functions. Three variants of the proposed algorithm are empirically evaluated on multiobjective knapsack benchmark problems and two real-world trauma system design problems. Experimental results demonstrate that the variant using RF models as the surrogates is effective and efficient in solving data-driven constrained multiobjective combinatorial optimization problems.
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