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

J. Yazdi, D. G. Yoo, Joong Hoon Kim

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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)1-10
Number of pages10
JournalUrban Water Journal
DOIs
Publication statusAccepted/In press - 2016 Sep 8

Fingerprint

urban drainage
drainage network
rehabilitation
comparative study
hydraulics
pipe
genetic algorithm
sorting
environmental protection
water management
flooding
replacement
water resource
engineering
water

Keywords

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

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

Cite this

Comparative study of multi-objective evolutionary algorithms for hydraulic rehabilitation of urban drainage networks. / Yazdi, J.; Yoo, D. G.; Kim, Joong Hoon.

In: Urban Water Journal, 08.09.2016, p. 1-10.

Research output: Contribution to journalArticle

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