Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

Urban water distribution networks are crucial infrastructures for providing essential services to society, but their exorbitant costs and limited water resources make their optimization a critical research area. Optimal management and design of these networks can help to reduce costs and enhance their efficiency while meeting technical, economic, and quality standards. In particular, the management of network pressure is critical for reducing leakage in water distribution systems. Thus, this study aimed to investigate two objective functions for optimizing the water distribution network: (i) minimizing costs while considering the number of pressure-relief valves, and (ii) minimizing network pressure by observing the optimal pressure range. To achieve this, the Nondominated Sorting Differential Evolution (NSDE) multi-objective metaheuristic algorithm was employed as the optimization tool, and a computer program was written in MATLAB software for solving the optimization models. EPANET software was also used for hydraulic simulation of the water distribution network. The efficiency and capabilities of these models were tested on the case study of the third district of Mashhad in Iran. The results indicated that the installation and adjustment of pressure-relief valves in accordance with the positions and optimal settings of the output of the proposed models significantly improved the desired goals, particularly the average pressure of the network. As an example of optimization, the study achieved a 56.12% reduction in pressure compared to the case without a plan, considering five pressure-relief valves.

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“…A comparative analysis for a benchmark problem with NSDE algorithm (Adapted from Refs. [20,[31][32][33][34]).…”

Section: Discussionmentioning

confidence: 99%

“…Considering alternative optimization techniques alongside NSDE can be beneficial for several reasons [31][32][33][34]:…”

Section: Alternative Techniquesmentioning

confidence: 99%

Optimal Exploitation of Urban Water Supply Networks Based on Pressure Management with the Nondominated Sorting Differential Evolution (NSDE) Algorithm

Cemiloglu

Licai

Ugurenver

et al. 2023

Water

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“…Traditional approaches had problems such as dependence on the starting point and entanglement in local minima. As a result, they could not find solutions that were close to optimal for complex, multi-objective pipe network issues [12]. In order to avoid local minima, researchers started to use soft computing techniques which employ meta-heuristic algorithms (such as genetic algorithms, simulated annealing, etc.)…”

Section: Introductionmentioning

confidence: 99%

Optimization of Water Distribution Systems Using Genetic Algorithms: A Review

Parvaze

Kumar

Khan

et al. 2023

Arch Computat Methods Eng

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Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms. Graphical Abstract

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Achieving sustainable water distribution through penalty‐free multi‐objective evolutionary design optimization

Kambalame,

Ndiritu

2024

World Water Policy

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Water distribution networks are costly long‐term investments, prompting researchers to seek cost reduction and efficient design solutions using optimization techniques. In this context, the Penalty‐Free Multi‐Objective Evolutionary Algorithm (PFMOEA) is employed alongside pressure‐dependent analysis (PDA) to optimize water distribution systems (WDSs). This integrated approach facilitates a multi‐objective evolutionary search, leading to improved WDS designs. The research aimed to improve the computational efficiency of PFMOEA by using real coding, where decision variables are represented as real numbers rather than integers or binary formats. Additionally, it adjusted the allocation of feasible and infeasible solutions near the Pareto front (the boundary of optimal or least‐cost solutions) during the elitism step of the optimization process. A feasible solution was regarded as the one that meets all the pressure and flow rate requirements while an infeasible solution fails to meet these requirements. These adjustments were labeled as PFMOEA‐A, PFMOEA‐B, and PFMOEA‐C, with allocation percentages of 15% feasible and 15% infeasible solutions, 20% feasible and 10% infeasible solutions, and 30% feasible and 0% infeasible solutions, respectively. The study utilized two benchmark network problems, the two‐looped and Hanoi networks, for analysis. A comparative assessment was then conducted to evaluate the performance of the real‐coded PFMOEA in comparison to other approaches documented in the literature. The algorithm demonstrated competitive performance for the two benchmark networks by implementing real coding. The real‐coded PFMOEA achieved the novel best‐known solutions ($419,000 and $6.081 million) and a zero‐pressure deficit for the two networks, requiring fewer function evaluations than the binary‐coded PFMOEA. Additionally, by replacing 15% of the feasible solutions with infeasible ones that are close to the Pareto front with minimal pressure deficit violations, the computational efficiency of the PFMOEA was enhanced. This led to a 20% and 17% reduction in the number of function evaluations required to identify the optimal solutions for the Two‐looped network and the Hanoi network, respectively. The findings of this study aim to contribute to a more equitable and resilient water management framework. Ultimately, the insights gained will not only support the optimization of existing water distribution networks but also inform policy decisions that prioritize sustainability and resource conservation. This holistic approach will facilitate improved water security and support the overarching goal of achieving sustainable water management against the growing challenges related to climate change and urbanization.HIGHLIGHTS PFMOEA eliminates the need for penalties through the adoption of a pressure‐dependent analysis within a multi‐objective optimization search. Employing real coding in PFMOEA instead of binary coding enhances the computational efficiency of PFMOEA. Retaining a small percentage of infeasible solutions near the boundary of optimal solutions during optimization also improves the computational performance of PFMOEA.

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Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

Cited by 5 publications

References 50 publications

Optimal Exploitation of Urban Water Supply Networks Based on Pressure Management with the Nondominated Sorting Differential Evolution (NSDE) Algorithm

Optimal Exploitation of Urban Water Supply Networks Based on Pressure Management with the Nondominated Sorting Differential Evolution (NSDE) Algorithm

Optimization of Water Distribution Systems Using Genetic Algorithms: A Review

Achieving sustainable water distribution through penalty‐free multi‐objective evolutionary design optimization

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