636
Views
26
CrossRef citations to date
0
Altmetric
Research Articles

Comparative study of multi-objective evolutionary algorithms for hydraulic rehabilitation of urban drainage networks

, &
Pages 483-492 | Received 07 Jan 2015, Accepted 11 Jul 2016, Published online: 08 Sep 2016

References

  • Barreto, W., Vojinovic, Z., Price, R., and Solomatine, D., 2010. Multi-objective evolutionary approach to rehabilitation of urban drainage systems. Journal of Water Resources Planning and Management, 136 (5), 547–554.
  • Coello Coello, C.A., 2011. An introduction to multi-objective particle swarm optimizers. In: J. Kacprzyk, ed. Soft Computing in Industrial Applications. Springer Berlin Heidelberg, 3–12.
  • Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T., 2000. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International Conference on Parallel Problem Solving From Nature. Springer Berlin Heidelberg, 849–858.
  • Farmani, R., Savic, D.A., and Walters, G.A., 2005. Evolutionary multi-objective optimization in water distribution network design. Engineering Optimization, 37 (2), 167–183.
  • Huff, F., 1990. Time Distributions of Heavy Rainstorms in Illinois, 173. Illinois State Water Survey, USA.
  • Kollat, J.B. and Reed, P.M., 2006. Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Advances in Water Resources, 29 (6), 792–807.
  • Maier, H.R., et al., 2014. Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling and Software, 62, 271–299.
  • Mugume, S.N., et al., 2015. A global analysis approach for investigating structural resilience in urban drainage systems. Water Research, 81 (2015), 15–26.
  • Nicklow, J., et al., 2010. State of the art for genetic algorithms and beyond in water resources planning and management. Journal of Water Resources Planning and Management, 136 (4), 412–432.
  • Rudenko, O. and Schoenauer, M., 2004. A steady performance stopping criterion for pareto-based evolutionary algorithms. In: Proceedings of the 6th International Multi-Objective Programming and Goal Programming Conference, Hammamet, Tunisia.
  • Rossman, L., 2008. Storm water management model user’s manual: Version 5.0., EPA/600/R-05/040. Cincinnati, OH: National Risk Management Research Laboratory.
  • Storn, R. and Price, K., 1995. DE a simple and efficient adaptive scheme for global optimization over continuous space, Technical Report TR-95-012, ICSI.
  • Sun, S.A., Djordjevic, S., and Khu, S.T., 2011. A general framework for flood risk-based storm sewer network design. Urban Water Journal, 8 (1), 13–27.
  • Wang, Q., Guidolin, M., Savic, D., and Kapelan, Z., , 2014. Two-objective design of benchmark problems of a water distribution system via MOEAs: towards the best-known approximation of the true Pareto front. Journal of Water Resources Planning and Management, 141 (3), 04014060. doi: 10.1061/(ASCE)WR.1943-5452.0000460
  • Yadav, A., Yadav, N. and Kim, J., 2016. A study of harmony search algorithms: Exploration and convergence ability. In: Harmony Search Algorithm. Seoul, South Korea: Springer Berlin Heidelberg, 53–62.
  • Yates, D.F., Templeman, A.B., and Boffey, T.B., 1984. The computational complexity of the problem of determining least capital cost designs for water supply networks. Engineering Optimization, 7 (2), 142–155.
  • Yazdi, J., Lee, E., and Kim, J., 2014. Stochastic multiobjective optimization model for urban drainage network rehabilitation. Journal of Water Resources Planning and Management, 141 (8), 04014091.
  • Yazdi, J., Sadollah, A., Lee, E.H., Yoo, D.G., and Kim, J.H., 2015. Application of multi-objective evolutionary algorithms for rehabilitation of storm sewer pipe networks. Journal of Flood Risk Management. doi: 10.1111/jfr3.12143
  • Zitzler, E., Deb, K. and Thiele, L., 2000. Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation, 8 (2), 173–195.
  • Zitzler, E., Laumanns, M., and Thiele, L., 2002. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: K. Giannakoglou, et al., eds. Evolutionary Methods for Design, Optimization and Control. Barcelona, Spain: International Center for Numerical Methods in Engineering (CIMNE), 95–100.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.