A multi-objective adaptive surrogate modelling-based optimization algorithm for constrained hybrid problems. (February 2022)

• Record Type: Journal Article
• Authors: Sun, Ruochen; Duan, Qingyun; Mao, Xiyezi
• Is Part Of: Environmental modelling & software. Volume 148(2022)
• Publication Date: 2022-02
• Subjects: Multi-objective optimization -- Surrogate model -- Constrained hybrid problem -- NSGA-II -- MO-ASMO; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105272
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Many multi-objective optimization problems in integrated environmental modelling and management involve not only continuous decision variables but also variables like integers and/or discrete variables. Furthermore, the optimization problems are often subject to various constraints. Solving this kind of constrained hybrid problems usually requires a huge number of model evaluations that can be computationally expensive. This study presents an algorithm known as multi-objective adaptive surrogate modelling-based optimization for constrained hybrid problems (MO-ASMOCH). It incorporates several evolutionary operators to handle different types of decision variables and uses a classification surrogate model to deal with model constraints. MO-ASMOCH was evaluated against the widely used NSGA-II method on three engineering design problems and three water distribution system design problems with up to 30 dimensions. The results showed that MO-ASMOCH is able to obtain nondominated solutions of similar quality as that of NSGA-II using much fewer model evaluations. Highlights: MO-ASMOCH is proposed to solve multi-objective constrained hybrid optimization problems. MO-ASMOCH handles various model constraints using a classification surrogate model. MO-ASMOCH has been evaluated on 6 benchmark problems with referenced Pareto fronts. MO-ASMOCH is as effective as but is much more efficient than NSGA-II.