Enhancing the Performance of a Multiobjective Evolutionary Algorithm for Sanitary Sewer Overflow Reduction
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 7
Abstract
The application of multiobjective evolutionary algorithms (MOEAs) to sanitary sewer overflow (SSO) optimization problems typically requires multiple runs of a simulation model and can be very computationally expensive. There is a need for simulation-optimization models that use fewer functional evaluations of the hydraulic model to identify near optimal solutions. In this study, two conflicting objectives were analyzed: maximizing SSO reduction and minimizing rehabilitation cost. This paper introduces a novel MOEA, the enhanced nondominated sorting evolution strategy (eNSES) that uses a specialized operator to guide the algorithm toward known SSOs locations. This strategy is being tested in an existing network in the eastern San Antonio Water System network. It has been compared with NSGA-II and NSES based on hypervolume and the overall nondominated vector generation ratio (ONVGR). The results show that eNSES improves the convergence rate by approximately 70% over the tested alternative algorithms, performing as well as NSGA-II and outperforming NSES in terms of the hypervolume by nearly 10%. In terms of the ONVGR, eNSES performed similarly to NSES but outperformed NSGA-II by 42%.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This project was funded by the San Antonio Water System (SAWS) under the inter-local agreement Development of an Optimization Framework for Sanitary Sewer Overflow Reduction, Contract No. CDM-14-040-MR. Special thanks to the Wastewater Master Planning team of the San Antonio Water System. Financial assistance from the U.S. Department of Agriculture/National Institute of Food and Agriculture (Award Number: 2014-38422-22088) to support students’ experiential learning is also gratefully acknowledged.
References
Beyer, H.-G., and Schwefel, H.-P. (2002). “Evolution strategies—A comprehensive introduction.” Nat. computing, 1(1), 3–52.
Bi, W., Dandy, G. C., and Maier, H. R. (2016a). “Use of domain knowledge to increase the convergence rate of evolutionary algorithms for optimizing the cost and resilience of water distribution systems.” J. Water Res. Plann. Manage., 142(9), .
Bi, W., Maier, H. R., and Dandy, G. C. (2016b). “Impact of starting position and searching mechanism on the evolutionary algorithm convergence rate.” J. Water Res. Plann. Manage., 142(9), .
Creaco, E., Franchini, M., and Walski, T. (2014a). “Accounting for phasing of construction within the design of water distribution networks.” J. Water Res. Plann. Manage., 598–606.
Creaco, E., Franchini, M., and Walski, T. M. (2014b). “Taking account of uncertainty in demand growth when phasing the construction of a water distribution network.” J. Water Res. Plann. Manage., .
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
EPA. (2004). “Report to congress: Impacts and control of CSOs and SSOs.” Washington, DC.
Friedrich, T., Neumann, F., and Thyssen, C. (2015). “Multiplicative approximations, optimal hypervolume distributions, and the choice of the reference point.” Evol. Comput., 23(1), 131–159.
Jiang, S., Ong, Y.-S., Zhang, J., and Feng, L. (2014). “Consistencies and contradictions of performance metrics in multiobjective optimization.” IEEE Trans. Cybernetics, 44(12), 2391–2404.
Kanta, L., Zechman, E., and Brumbelow, K. (2012). “Multiobjective evolutionary computation approach for redesigning water distribution systems to provide fire flows.” J. Water Res. Plann. Manage., 144–152.
Knowles, J., and Corne, D. (2002). “On metrics for comparing nondominated sets.” Proc., Congress on Evolutionary Computation, 2002 CEC’02, Vol. 1, IEEE, Piscataway, NJ, 711–716.
Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1999). “Approximating the Pareto set: Concepts, diversity issues, and performance assessment.” Int. J. Bio-Inspired Comput., 7(1), 1–25.
Liang, L. Y., Thompson, R. G., and Young, D. M. (2004). “Optimising the design of sewer networks using genetic algorithms and tabu search.” Eng., Constr. Archit. Manage., 11(2), 101–112.
Moeini, R., and Afshar, M. (2013). “Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems.” J. Hydroinf., 15(1), 155–173.
Nguyen, D. C. H., Dandy, G. C., Maier, H. R., and Ascough, J. C. (2016). “Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge.” J. Water Res. Plann. Manage., 142(9), .
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Res. Plann. Manage., 412–432.
Ogidan, O., and Giacomoni, M. (2016). “Multiobjective genetic optimization approach to identify pipe segment replacements and inline storages to reduce sanitary sewer overflows.” Water Res. Manage., 30(11), 3707–3722.
Rathnayake, U. S., and Tanyimboh, T. T. (2015). “Evolutionary multi-objective optimal control of combined sewer overflows.” Water Res. Manage., 29(8), 2715–2731.
Rossman, L. A. (2010). “Storm water management model user’s manual.” National Risk Management Research Laboratory, Cincinnati.
Seada, H., and Deb, K. (2016). “A unified evolutionary optimization procedure for single, multiple, and many objectives.” IEEE Trans. Evol. Comput., 20(3), 358–369.
Simpson, A. R., Dandy, G. C., and Murphy, L. J. (1994). “Genetic algorithms compared to other techniques for pipe optimization.” J. Water Res. Plann. Manage., 423–443.
Sun, S., Djordjević, S., and Khu, S.-T. (2011). “A general framework for flood risk-based storm sewer network design.” Urban Water J., 8(1), 13–27.
Tang, Y., Reed, P., and Wagener, T. (2005). “How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?” Hydrol. Earth Syst. Sci. Discuss., 2(6), 2465–2520.
Wu, Z. Y., and Simpson, A. R. (2001). “Competent genetic-evolutionary optimization of water distribution systems.” J. Comput. Civ. Eng., 89–101.
Zechman, E. M., and Ranjithan, S. R. (2009). “Evolutionary computation-based methods for characterizing contaminant sources in a water distribution system.” J. Water Res. Plann. Manage., 334–343.
Zitzler, E., and Thiele, L. (1999). “Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach.” IEEE Trans. on Evol. Comput., 3(4), 257–271.
Information & Authors
Information
Published In
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jun 24, 2016
Accepted: Jan 13, 2017
Published ahead of print: Mar 24, 2017
Published online: Mar 25, 2017
Published in print: Jul 1, 2017
Discussion open until: Aug 25, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.