Optimal reservoir flood operation using a decomposition-based multi-objective evolutionary algorithm. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Zhang, Xiao; Luo, Jungang; Sun, Xiaomei; Xie, Jiancang
• Is Part Of: Engineering optimization. Volume 51:Issue 1(2019)
• Page Start: 42
• Page End: 62
• Publication Date: 2019-01-02
• Subjects: Reservoir flood control operation -- multi-objective evolutionary algorithm -- decomposition -- differential evolution; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1439942
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Reservoir flood control operation (RFCO) is a challenging optimization problem with interdependent decision variables and multiple conflicting criteria. By considering safety both upstream and downstream of the dam, a multi-objective optimization model is built for RFCO. To solve this problem, a multi-objective optimizer, the multi-objective evolutionary algorithm based on decomposition–differential evolution (MOEA/D-DE), is developed by introducing a differential evolution-inspired recombination into the algorithmic framework of the decomposition-based multi-objective optimization algorithm, which has been proven to be effective for solving complex multi-objective optimization problems. Experimental results on four typical floods at the Ankang reservoir illustrated that the suggested algorithm outperforms or performs as well as the comparison algorithms. It can significantly reduce the flood peak and also guarantee the dam's safety.