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

J. Yazdi, D. G. Yoo, J. H. Kim

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Multi-Objective Evolutionary Algorithms (MOEAs) are flexible and powerful tools for solving a wide variety of non-linear and non-convex problems in water resources engineering contexts. In this work, two well-known MOEAs, the Strength Pareto Evolutionary Algorithm (SPEA2) and Non-dominated Sorting Genetic Algorithm (NSGA2), and two additional MOEAs that are extended versions of harmony search (HS) and differential evolution (DE), are linked to the Environmental Protection Agency’s Storm Water Management Model (SWMM-EPA), which is a hydraulic model used to determine the best pipe replacements in a set of sewer pipe networks to decrease urban flooding overflows. The performance of the algorithms is compared for several comparative metrics. The results show that the algorithms exhibit different behaviours in solving the hydraulic rehabilitation problem. In particular, the multi-objective version of the HS algorithm provides better optimal solutions and clearly outperforms the other algorithms for this type of nondeterministic polynomial-time hard (NP-hard) problem.

Original languageEnglish
Pages (from-to)483-492
Number of pages10
JournalUrban Water Journal
Issue number5
Publication statusPublished - 2017 May 28

Bibliographical note

Funding Information:
This work was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) [grant number 2016R1A2A1A05005306].

Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.

Copyright 2017 Elsevier B.V., All rights reserved.


  • DE
  • HS
  • MOEA
  • NSGA2
  • SPEA2
  • urban drainage system

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology


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