A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems

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 optimizatio...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 50; H. 2; S. 536 - 549
Hauptverfasser: Wang, Handing, Jin, Yaochu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Combinatorial analysis
Computational modeling
Constrained multiobjective combinatorial optimization
Constraint modelling
data-driven optimization
evolutionary algorithm (EA)
Evolutionary algorithms
Forestry
Linear programming
Mathematical programming
Multiple objective analysis
Optimization
Radial basis function
radial basis function (RBF) networks
Radio frequency
random forest (RF)
Regression analysis
Regression models
surrogate
Systems design
Training
trauma systems
Vegetation
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung: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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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2018.2869674