WDN

Assessing the Performance of Surrogate Measures for Water Distribution Network Reliability

This paper investigates the use of surrogate measures as potential substitutes for reliability in multiobjective design of water distribution networks (WDNs). Assessing WDN reliability with conventional hydraulic and mechanical reliability metrics may require substantial computational time and resources, which becomes more critical as the network size increases.

SaDE algorithm for reliability-based design of water distribution networks

Design of Water Distribution Networks (WDNs) is an NP hard problem, which is typically an optimization problem consisting of a large search space even for a small sized WDN. Design of a robust WDN requires consideration of aspects such as reliability, resiliency etc.

Examining resiliency and entropy as surrogate measures for hydraulic reliability estimation of water distribution networks

Reliability-based design of water distribution networks using self-adaptive differential evolution algorithm

This paper presents the application of Self-Adaptive Differential Evolution (SADE) algorithm for reliability-based design of Water Distribution Networks (WDNs). The algorithm is first applied and tested for deterministic design of various benchmark WDNs.

Multi-Objective Design of Water Distribution Networks using Resiliency as Reliability Surrogate

Multi-Objective Self-Adaptive Differential Evolution Algorithm for design of water distribution networks

Reliability-based design of water distribution networks considering mechanical failures

For effective design of Water Distribution Networks (WDNs), it is necessary to consider the reliability of the system subject to mechanical and hydraulic failures. In this paper, a reliability based framework is presented for design of WDNs using self-adaptive differential evolution (SaDE) algorithm.

Reliability assessment of water distribution networks accounting for pipe failure

Determining optimal location of isolation valves using self-adaptive differential evolution algorithm