Cluster-based multi-objective optimization for identifying diverse design options: Application to water resources problems. (January 2021)

• Record Type: Journal Article
• Authors: Sahraei, Shahram; Asadzadeh, Masoud
• Is Part Of: Environmental modelling & software. Volume 135(2021)
• Publication Date: 2021-01
• Subjects: Multi-objective optimization -- Decision-space diversity -- Dynamic clustering -- DBSCAN -- Water resources -- Engineering design; 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.2020.104902
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In this study, a novel density-based spatial clustering method is developed to maintain a diverse set of solutions for stochastic multi-objective optimization algorithms. This method dynamically clusters solutions in the decision space after solutions evaluations. Dominance check is localized to maintain solutions that are globally dominated but locally non-dominated in their cluster. Unlike the original solution archiving, the proposed method implemented for Pareto Archived-Dynamically Dimensioned Search successfully finds optimal and near-optimal fronts with different cluster labels in two mathematical case studies. Two environmental benchmark problems are also solved and a three-stage screening process is applied to their archive sets to identify the number of dissimilar options. The dissimilarity index devised for this study shows a significantly higher distinction level and archive size for the cluster-based solution archiving, which allows decision-makers to have higher flexibility in refining their preferences for robust decision-making in the environmental problems, compared with the original archiving. Highlights: Decision-space diversity maintenance is needed to find distinct design options. Decentralized dominance-check helps detect optimal and near-optimal tradeoffs. Cluster-based archiving preserves solution dissimilarity in decision space. Cluster-based optimization finds a large set of high-quality design options. Cluster-based optimization gives flexibility to decision-maker for robust decisions.