Abstract
ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.
Highlights
Water distribution networks (WDNs) are hydraulic systems composed of reservoirs, pumps, pipes, valves, sensors and other accessories designed to transport drinking water with sufficient flow and pressure to meet consumers’ water needs continuously and appropriately
The particle swarm optimization (PSO) and hybrid particle swarm optimization and tabu search (H-PSOTS) algorithms were applied to three complex WDNs referenced in the literature
The H-PSOTS algorithm proved to be a good option for the optimal design of large WDNs
Summary
Water distribution networks (WDNs) are hydraulic systems composed of reservoirs, pumps, pipes, valves, sensors and other accessories designed to transport drinking water with sufficient flow and pressure to meet consumers’ water needs continuously and appropriately. WDN designers seek to assign pipe diameters to minimize investment costs, including those related to implementing and purchasing materials and equipment. Engineers design pipe networks using trial and error guided by experience. WDN design is classified as a large combinatorial discrete nonlinear nondeterministic polynomial-hard (NP-hard) optimization problem (Moosavian & Lence, 2020). Such problems are nonlinear and non-convex, and global optimization techniques cannot usually solve them. The development of new models makes a substantial contribution to solving this type of problem (Cassiolato et al, 2020)
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