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<p>Water distribution networks (WDNs) represent critical infrastructure systems that connect sources of water to end consumers, where cost optimization remains a fundamental challenge owing to the substantial construction investments required. This study introduces FISANET, a self-adaptive, non-metaphorical optimization framework for the cost-effective design of a WDN. The framework is implemented in a Python environment and is integrated with the EPANET 2.2 hydraulic solver, which eliminates the need for complex parameter tuning while maintaining the computational efficiency. The Performance is evaluated on three established benchmark networks, namely the Two-Loop Network (TLN), Hanoi Network (HN), and New York Tunnel Network (NYTN), along with a real-world field WDN of the Planning and Architecture School (SPAN), Bhopal, Madhya Pradesh, India. The results demonstrate that FISANET achieves optimized cost solutions with significantly fewer function evaluations compared to other existing approaches. Moreover, this pressure-driven demand analysis (PDA) integrated with the FISANET framework consistently outperformed traditional demand-driven analysis (DDA) approaches, achieving a cost reduction of 1.62% and identifying the new best solution for the NYTN network, while also achieving the best-known solutions for the TLN and HN networks. This nature of FISANET reduces the significant computational effort required for algorithm calibration, making it highly suitable for practical engineering applications that require rapid convergence and reliability in infrastructure design.</p>

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A Novel Self-Adaptive, Non-Metaphor-Based FISANET Framework for Pressure Dependent Optimization of Water Distribution Networks

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Vishal Jain,
  • Ruchi Khare

摘要

Water distribution networks (WDNs) represent critical infrastructure systems that connect sources of water to end consumers, where cost optimization remains a fundamental challenge owing to the substantial construction investments required. This study introduces FISANET, a self-adaptive, non-metaphorical optimization framework for the cost-effective design of a WDN. The framework is implemented in a Python environment and is integrated with the EPANET 2.2 hydraulic solver, which eliminates the need for complex parameter tuning while maintaining the computational efficiency. The Performance is evaluated on three established benchmark networks, namely the Two-Loop Network (TLN), Hanoi Network (HN), and New York Tunnel Network (NYTN), along with a real-world field WDN of the Planning and Architecture School (SPAN), Bhopal, Madhya Pradesh, India. The results demonstrate that FISANET achieves optimized cost solutions with significantly fewer function evaluations compared to other existing approaches. Moreover, this pressure-driven demand analysis (PDA) integrated with the FISANET framework consistently outperformed traditional demand-driven analysis (DDA) approaches, achieving a cost reduction of 1.62% and identifying the new best solution for the NYTN network, while also achieving the best-known solutions for the TLN and HN networks. This nature of FISANET reduces the significant computational effort required for algorithm calibration, making it highly suitable for practical engineering applications that require rapid convergence and reliability in infrastructure design.