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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Vydáno v:	IEEE transactions on cybernetics Ročník 50; číslo 2; s. 536 - 549
Hlavní autoři:	Wang, Handing, Jin, Yaochu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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. 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.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.
Author	Jin, Yaochu Wang, Handing
Author_xml	– sequence: 1 givenname: Handing orcidid: 0000-0002-4805-3780 surname: Wang fullname: Wang, Handing email: hdwang@xidian.edu.cn organization: Department of Computer Science, University of Surrey, Guildford, U.K – sequence: 2 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu email: yaochu.jin@surrey.ac.uk organization: Department of Computer Science, University of Surrey, Guildford, U.K
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Snippet	Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for...
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SubjectTerms	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
Title	A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems
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