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

• Published in: IEEE transactions on cybernetics Vol. 50; no. 2; pp. 536 - 549
• Main Authors: Wang, Handing, Jin, Yaochu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TCYB.2018.2869674

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.