Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems

Wang, Qi; Zhou, Qianqian; Lei, Xiaohui; Savić, Dragan

doi:10.1061/(asce)wr.1943-5452.0000996

Cited by 19 publications

(12 citation statements)

References 50 publications

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“…The parameterization impact of NSGA-II is illustrated in the format of a compass plot initially used in [34]. Generally speaking, the compass plot is used to demonstrate how the performance of NSGA-II varies according to different combinations of its parameters.…”

Section: Proposed Methodology Of Investigating the Parameterization Omentioning

confidence: 99%

Parameterization of NSGA-II for the Optimal Design of Water Distribution Systems

Wang

Huang

et al. 2019

Water

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The optimal design of Water Distribution Systems (WDSs) using multi-objective evolutionary algorithms (MOEAs) has received substantial attention in the past two decades. Many MOEAs have been proposed and applied successfully to this challenging problem. However, these tools are primarily considered black-boxes by end users, especially when the algorithm parameterization issues are taken into consideration. This paper presents a simple yet effective method for capturing the interrelationships among the five key parameters of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of the state-of-the-art MOEAs in this field. Two representative boundary values for each parameter are selected from a reasonable range, and all the possible combinations are tested on three benchmark design problems. Those benchmarks are based on two widely used small networks and a larger, real-world irrigation network. Results suggest that there is a hierarchy of impacts imposed by the five parameters of NSGA-II. The population size turns out to be the most important one, which implies that NSGA-II is sensitive to the initial population, especially for complex problems. A relatively large population size increases the diversity of a population; hence some key genes may be identified at the beginning (or early stage) of search. Furthermore, it transpires that the distribution indices of crossover and mutation have a more significant impact than their probabilities, where the former are generally overlooked by previous studies. Some useful guidelines are also provided, which can improve the efficacy of NSGA-II and increase the chance of identifying near-optimal solutions.

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Section: Proposed Methodology Of Investigating the Parameterization Omentioning

confidence: 99%

Parameterization of NSGA-II for the Optimal Design of Water Distribution Systems

Wang

Huang

et al. 2019

Water

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“…The flood damage cost function represents damages caused by the flooding. Some authors consider flooding as a volume ( [34]); however, others present flooding as the highest depth reached by the water (y) out of the network. Lee and Kim [19] showed that flood damage is different from flood volume.…”

Section: Objective Functionsmentioning

confidence: 99%

Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

Ngamalieu-Nengoue

Martínez-Solano

Iglesias-Rey

et al. 2019

Water

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Drainage networks are civil constructions which do not generally attract the attention of decision-makers. However, they are of crucial importance for cities; this can be seen when a city faces floods resulting in extensive and expensive damage. The increase of rain intensity due to climate change may cause deficiencies in drainage networks built for certain defined flows which are incapable of coping with sudden increases, leading to floods. This problem can be solved using different strategies; one is the adaptation of the network through rehabilitation. A way to adapt the traditional network approach consists of substituting some pipes for others with greater diameters. More recently, the installation of storm tanks makes it possible to temporarily store excess water. Either of these solutions can be expensive, and an economic analysis must be done. Recent studies have related flooding with damage costs. In this work, a novel solution combining both approaches (pipes and tanks) is studied. A multi-objective optimization algorithm based on the NSGA-II is proposed for the rehabilitation of urban drainage networks through the substitution of pipes and the installation of storage tanks. Installation costs will be offset by damage costs associated with flooding. As a result, a set of optimal solutions that can be implemented based on the objectives to be achieved by municipalities or decisions makers. The methodology is finally applied to a real network located in the city of Bogotá, Colombia.

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“…In the literature, flooding is considered to be a volume for some authors [39] while other authors consider flooding as the highest depth reach by the water (y) out of the network. According to Lee and Kim [40], flooding damage is different from flooding volume.…”

Section: Flood Damage Cost Functionmentioning

confidence: 99%

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

2019

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The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

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Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems

Cited by 19 publications

References 50 publications

Parameterization of NSGA-II for the Optimal Design of Water Distribution Systems

Parameterization of NSGA-II for the Optimal Design of Water Distribution Systems

Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

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