Scholarly Articles

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

Talor & Francis

Author

노준우,J. Yaz,김중훈

Publication Date

20170501

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 wellknown
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.