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Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms

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Traditional multi-objective evolutionary algorithms treat each objective equally and search randomly in all solution spaces without using preference information. This might reduce the search efficiency and quality of solutions preferred by decision makers, especially when solving problems with complicated properties or many objectives. Three reference point based algorithms which adopt preference information in optimization progress, e.g., R-NSGA-II, r-NSGA-II and g-NSGA-II, have been shown to be effective in finding more preferred solutions in theoretical test problems. However, more efforts are needed to test their effectiveness in real-world problems. This study conducts a comparison of the above three algorithms with a standard algorithm NSGA-II on a reservoir operation problem to demonstrate their performance in improving the search efficiency and quality of preferred solutions. Under the same calculation times of the objective functions, Pareto optimal solutions of the four algorithms are used in the empirical comparison in terms of the approximation to the preferred solutions. Three performance indicators are then adopted for further comparison. Results show that R-NSGA-II and r-NSGA-II can improve the search efficiency and quality of preferred solutions. The convergence and diversity of their solutions in the concerned region are better than NSGA-II, and the closeness degree to the reference point can be increased by 42.8%, and moreover the number of preferred solutions can be increased by more than 3 times when part of objectives are preferred. By contrast, g-NSGA-II shows worse performance. This study exhibits the performance of three reference point based algorithms and provides insights in algorithm selection for multi-objective reservoir optimization problems.

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This study is supported by the National Natural Science Foundation of China (Grant No. 91747102, 51709036, 91647201, 51579027) and the last author’s Royal Society Industry Fellowship (Ref: IF160108). K. Li is supported by UKRI Future Leaders Fellowship (Ref: MR/S017062/1).

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Correspondence to Wei Ding.

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Tang, R., Li, K., Ding, W. et al. Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms. Water Resour Manage 34, 1005–1020 (2020).

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