Multi-objective Evolutionary Algorithms Assessment for Pump Scheduling Problems

The shortage of drinking water is one of the biggest problems facing humanity today. Solving this problem necessarily involves an optimal use of this resource, starting from the pumping. Determining the water pumping regime to meet the demands of a city is a multi-objective complex problem. One of the steps to solve this problem is assessing which multi-objective optimizer has better performance. In this work, we provide a methodology for the comparison of multi-objective evolutionary algorithms in the water pumping regime optimization problem through the combination of the EPANET and the jMetal framework. Both were validated in the comparison of NSGA-II, SPEA2, and SMPSO to optimize the pumping regime on the water distribution networks Van Zyl, Baghmalek, and Anytown. The quality indicators Spread, Epsilon, and Hypervolume, allow assessing the superiority/competitivity statistically of one method over others in terms of solutions' convergence and distribution. The experimental results show that the combination of EPANET and jMetal provide the ideal environment to perform MOEAs comparisons effectively.