Structural and empirical knowledge driven multi-objective evolutionary algorithm for urban drainage system design

Abstract

Urban drainage system plays an important role in urban rainwater management. The design of urban drainage system is challenging for its complex constraints and expensive individual evaluation. A kind of multi-objective evolutionary algorithm driven by both structural and empirical knowledge, called structural and empirical knowledge driven multi-objective evolutionary algorithm, is proposed in this paper. A novel tree-template-based encoding schema is proposed to extract the structural knowledge of urban drainage system. The population initialization method and genetic operators (i.e., crossover and mutation) are customized to handle the constraints among decision variables caused by the structure restriction of the urban drainage system. The radial basis function (RBF) model is employed to learn the empirical knowledge from the previous evolution process, and the RBF-based approximate evaluation is used to partially replace the expensive individual evaluation to improve the efficiency of the algorithm. Besides, by combining the decomposition idea and the constrained dominance principle, the local constrained dominance principle is suggested to handle the constraints in decision space, which can effectively improve the defect that the constrained dominance principle is easy to fall into local optimum. Comprehensive experiments on two urban drainage system cases show the superiority of the proposed algorithm for the optimal urban drainage system design.