Application of Fine-Tuned Krill Herd Algorithm in Design of Water Distribution Networks

Abstract

Stochastic population-based metaheuristic algorithms have been widely used to design water distribution networks (WDNs). The present study introduces one of its kind, the krill herd algorithm (KHA), a novel swarm intelligence-based metaheuristic. Demonstrating KHA application to WDN design, the study proposes its enhanced version to improve its convergence precision. Consequently, combining the fine-tuned mechanism of KHA with the EPANET 2.2 simulation software, the fine-tuned KHA (FIT-KHA) model is formulated and checked for its efficiency in handling the WDN design problem. Because three benchmark problems (new and rehabilitation WDNs) of different dimensions are considered, the significance of the algorithm control parameters concerning the network size is studied. From sensitivity analysis, the time step factor, number of krill, and maximum iteration size are found to influence the algorithm performance. Their optimal values followed an increasing trend with the network size. The computational results reinforce a superior search exploitation ability of the FIT-KHA. Importantly, the results exhibit a better computational efficiency of KHA over most of the reported metaheuristic algorithms. Then, the performance state of optimally designed WDN under hydraulic (demand, roughness coefficient variations) and mechanical (single- and two-pipe failure) uncertain scenarios is studied. Besides reliability and surrogate measures, multiaspect metrics based on adequacy and equity are evaluated to assess the network’s performance. The WDNs are observed to be more susceptible to demand uncertainties over roughness coefficient variation. At higher states of mechanical (pipe) failure scenarios, especially, the equity of the WDN is disturbed compared to the adequacy, except for rehabilitated WDN. Certainly, the performance study manifests that the reliability measure that truly presents the WDNs performance is the function of network type. While proposing KHA as an effective alternative optimization tool, the study suggests a prior performance study to choose an appropriate metric to formulate a reliability-based multiobjective framework for the robust design of WDNs. Water distribution networks (WDNs) form an essential infrastructure of the society conveying potable water. Considering the nondeterministic polynomial-time hard (NP-Hard) nature of the WDN design problem, extensive research has been conducted proposing various optimization algorithms for its design. The present study introduces a novel swarm intelligence-based metaheuristic optimization technique, the krill herd algorithm (KHA). Demonstrating the excellent exploitation ability of KHA, the study formulated its enhanced version, the fine-tuned KHA (FIT-KHA) model. Its computational efficiency over other reported algorithms in handling the new and rehabilitated WDNs of different dimensions is demonstrated. Considering the FIT-KHA model optimal designs, the benchmark WDNs performance state under hydraulic (demand, roughness coefficient variations) and mechanical (single- and two-pipe failures) uncertain scenarios are also studied. The WDNs are more susceptible to demand uncertainties and higher states of mechanical (pipe) failure scenarios over roughness coefficient variation. The reliability measure that truly presents the WDNs performance is the function of network type. Hence, proposing KHA as an effective alternative optimization tool, the study suggests a prior performance study to choose an appropriate metric to formulate a reliability-based multiobjective framework for the robust design of WDNs.