Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown exceptional abilities in solving expensive constrained optimization problems (ECOPs). However, few of them are designed for ECOPs involving mixed...
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Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown exceptional abilities in solving expensive constrained optimization problems (ECOPs). However, few of them are designed for ECOPs involving mixed integer variables (ECOPs-MI). Therefore, a two-layer surrogate-assisted collaborative framework called TLSACF is designed, in which the radial basis function (RBF) and Gaussian Process (GP)-based mixed-integer collaborative frameworks are combined effectively. In the RBF-based collaborative framework, the combination between RBFassisted prescreening and RBF-based local search achieves effective population update by balancing the exploration and exploitation on high potential parental information. For the GPbased collaborative framework, a variable type-based cooperative mutation is utilized to obtain potential candidate individuals by assigning targeted mutation for different variables, and a hypervolume-based complete expected improvement matrix function is derived to prescreen these candidate solutions from the perspective of balancing the optimization on constraints and the objective for the effective search on disconnected sub-feasible regions caused by integervariables. Experimental results on ten classical problems show that TLSACF performs excellent in solving ECOPs-MI.
作者：
Kalitventzeff, B.University of Liège
Belsim s.a. Centre Socran Parc industriel du Sart-Tilman 4900 (Angleur) Bat B6 4000 Liege Avenue Pré-Aily Belgium
This paper will focus on the application of MlNLP (mixedinteger Non Linear Programming). For the sake of application, we have chosen the utility network management problem which is important to mechanical and chemica...
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