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Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such as particle swarm optimization (PSO), multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, a lack of comparison between these approaches poses a challenge to the selection of optimization approach for stormwater drainage pumping stations. In this paper, a new framework for comparing multi-objective approaches is proposed. Two kinds of evolutionary approaches, single-objective optimization and multi-objective optimization, are considered. Three approaches representing these two types are selected for comparison, including PSO with linear weighted sum method (PSO-LWSM), MOPSO with technique for order preference by similarity to an ideal solution (MOPSO-TOPSIS), and NSGA-II with TOPSIS (NSGA-II-TOPSIS). Four optimization objectives based on the number of pump startups/shutoffs, working hours, energy consumption, and drainage capacity are considered, of which the first two are new ones quantified in terms of operational economy in this paper. Two comparison methods—TOPSIS and operational economy and drainage capacity (E&amp;C)—are used. The framework is demonstrated and tested by a case in China. The average values of the TOPSIS comprehensive evaluation index of the three approaches are 0.021, 0.154, and 0.375, respectively, and for E&amp;C are 0.785, 0.813, and 0.839, respectively. The results show that the PSO-LWSM has better optimization results. The results validate the efficiency of the framework. The proposed framework will help to find a better optimization approach for pumping systems to reduce energy consumption and maintenance costs.

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A Framework for Comparing Multi-Objective Optimization Approaches for a Stormwater Drainage Pumping System to Reduce Energy Consumption and Maintenance Costs