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Evolutionary Computation
Fall 2011, Vol. 19, No. 3, Pages 429-467
(doi: 10.1162/EVCO_a_00035)
© 2011 by the Massachusetts Institute of Technology
Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks
Article PDF (1.28 MB)
Manuel López-Ibáñez
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium manuel.lopez-ibanez@ulb.ac.be
T. Devi Prasad
School of Computing, Science & Engineering, University of Salford, UK d.p.tumula@salford.ac.uk
Ben Paechter
Centre for Emergent Computing, School of Computing, Edinburgh Napier University, UK b.paechter@napier.ac.uk
Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels; or explicitly, by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain fewer than the maximum number of switches. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.