The Water Distribution Network (WDN) is one of the main smart utilities considered in every smart city. Water conservation on any given day can be achieved using Internet of Things (IoT) technology to make the conventional citywide WDN smart. This will simplify recognizing trends in water consumption, quality, and leaks in real-time. The most important considerations in designing and operating a WDN are: 1) recognizing the city's dynamic demand requirements while taking into account its ongoing expansion [1], 2) providing customers with sufficient quantity of water to suit their consumption requirements [2], and 3) identifying anomalous water network situations, such as leaks, pipe breaks, valve, meter, etc., and prioritizing their scheduling, and, keeping an eye on the water quality that is supplied to customers and supporting treatment cycles in the case of contaminated water [3]. The development of smart water is urgently needed. Networks that adapt to demands keep an eye on crucial variables like altitude, pressure, flow rate, and water quality measurements, and they identify line leaks [4]. Additionally, this needs the information to flow from every part of the water system to a central observation point where it can be used to evaluate the information and send out warnings to interested authorities for prompt action.

An IoT-driven architecture was created, experiments were conducted, and a water test bed was established with an emphasis on creating smart water meters and billing systems for residential customers, remotely operated automatic distribution valves, and systems for water quality monitoring carried out by using analytics on the network data that was gathered [5]. The use of metaheuristic algorithms expands significantly as the temporal and spatial complexity of environmental issues and water resources management rises. Since ACO was first introduced [6], different versions and refinements of the original ant algorithm have been proposed and applied to solve various water resource problems and environmental management problems. Ants' collective behavior in their quest for food is the basis for the discrete combinatorial optimization method known as ACO. An indirect method of communication involving deposition is thought to allow an ant colony to determine the quickest path from its nest to a food supply of a chemical compound known as a pheromone on the pathways during their journey [7]. With time, shorter and more pheromones are used to promote more preferred pathways, making them the colony's dominant route. Ant Colony Optimization (ACO) was initially developed for discrete optimization problems. Its application in the continuous domain started with the discretization of the search space. Although some advances in the discretization methodology, such as the Discrete-Refining (DR) approach, have been reported to improve the performance of the original ACOs in continuous domains, their comparative performances remain to be improved [8]. A new version of the ACO-based algorithm has been developed to overcome this issue. The main benefit of ACO algorithms in reservoir operation, as with any search-based algorithms, is their ability to be readily connected to any comprehensive and detailed system simulation model. To determine rule curve parameters and/or the best times for record releases, various ACO versions have been used [9]. Although the costly processing requirements of ACO algorithms may restrict their applicability for implicit and/or explicit stochastic optimization applied to numerous reservoir systems unless operational strategies can be adjusted, these methods are quite robust in solving highly nonlinear, non-convex problems [10]. Despite its popularity, mathematical analysis of the algorithm lags. Its popularity is partly attributed to its potential to solve nonlinear, non-convex, and multi-modal in discrete and continuous domains for which deterministic search techniques incur difficulty or fail [11].

A smart city can be defined as a municipality in which an investment in human and social capital is performed by encouraging the use of Information and Communication Technology (ICT) as an enabler of sustainable economic growth, improving the quality of life for its citizens and the efficiency of its operations, and consequently, allowing better management of energy and natural resources [12]. This innovative approach not only leverages technology but also prioritizes the well-being of its residents. Thus, the idea of a smart city extends beyond just technological advancements; it also focuses on fostering socio-economic development [13], allowing a city to assess its current status and identify areas needing improvement to fulfill the criteria for becoming a smart city [14]. Water management has emerged as a paramount concern among the critical areas for development. With pressing demands for the sustainable use of scarce water resources and water distribution systems, water management for smart cities emphasizing financial and environmental sustainability is becoming more valued across the water sector [15].

Smart water management systems can improve many existing urban networks characterized by degraded infrastructure, irregular supply, and low customer satisfaction or substantial deviations of the proportional bills from real consumption [16]. Implementing such systems, which are carried out through data collection, analysis, and control technologies, helps public service companies build a complete database for identifying the areas where water losses or illegal connections occur. This, in turn, contributes to leakage detection and correction, water quality assurance, improved consumer experience, and operational optimization, amongst other key benefits [17]. Implementing a smart water management system usually involves deploying multiple sensor and actuator nodes throughout the water distribution system. Sensor nodes play a vital role in collecting water flow data from various parts of the distribution network, enabling water usage tracking and system fault identification [18]. Data collection is performed manually, supplemented by IoT technologies for data communication and data analytics for processing. The process includes monitoring water levels in the tanks and sending the readings to a server via Arduino or Raspberry Pi, with results visualized through a web interface like Ubidots [19]. The integration of ICT has thus addressed the challenges urban water networks face by facilitating continuous water distribution system monitoring [20].

It has been estimated that approximately 80% of the total cost of a water supply project is invested in its water distribution system [13]. Therefore, designing a cost-effective and reliable WDN is essential. The optimization of a WDN involves the design of a reliable, efficient, and cost-effective distribution network that meets the necessary water demand while maintaining adequate pressure heads. This optimization is crucial for conserving water resources and reducing energy requirements and maintenance costs. WDN optimization can be categorized into several types: design, operation, calibration, level-of-service, monitoring system, and network testing [14]. Recognizing the importance of operational costs, many researchers have focused on pump scheduling to reduce energy costs by taking advantage of off-peak electricity and reservoir storage in water distribution systems. In parallel with applying Genetic Algorithms (GAs) and other search-based algorithms, some researchers have also focused on using various concepts based on Swarm Intelligence (SI) to optimize pumping stations in WDNs [15].

SI is a field inspired by the social behaviors of insects and animals, such as ants, bees, and fireflies, to develop algorithms for solving complex optimization problems. One such algorithm, ACO has achieved good effect in addressing problems such as the Traveling Salesman Problem (TSP), Quadratic Assignment Problem (QAP), Job-Shop Scheduling Problem (JSSP), and many more. The algorithm was primarily intended to solve combinatorial optimization problems, among which NP-hard problems (such as those mentioned previously) were the most challenging. Since no polynomial-time algorithms are known for such problems, heuristic techniques such as ACO are often used for generating high-quality solutions in reasonable computation times [21]. An improvement to current network-routing analysis methods is suggested by implementing intelligent path-traversing algorithms, particularly artificial agent technologies, one of which is the ACO algorithm. This metaheuristic mimics the collective behavior of real-life ant colonies. This algorithm is characterized by a collective knowledge-processing system that relies on information acquired by individual ants as they explore potential paths during the 'trail laying' phase; the colony then memorizes the processed information in the 'trail following' phase, searching for path solutions to a destination. Over time, this process converges on an optimal solution to the path traversal problem, akin to techniques used in pipe routing and network optimization [22].

As the spatial and temporal complexity of resource management and environmental issues increases, the use of metaheuristic algorithms has expanded dramatically. Among its various applications, the ACO algorithm has garnered considerable attention in water resources and environmental planning and management over the last decade. Since its introduction, different versions and refinements to the original algorithm have been proposed and applied to address various challenges in these fields [23]. The algorithm was first used for hydroelectric generation scheduling by Huang [24] and later optimized for reservoir operation by Jalali et al. [25], incorporating enhancements such as pheromone promotion and explorer ants. Kumar and Reddy [26] also utilized ACO to derive operating policies in a multi-purpose reservoir system [23]. In the earliest application of ACO in WDN optimization, the algorithm was employed for the optimal design of a small network comprising fourteen pipes, two reservoirs, and three loops, focusing on demonstrating the application of ACO in WDN design and investigating the sensitivity of the method to changes in tunable parameters [27].

Optimizing water distribution in smart cities is crucial for efficient resource management and energy conservation. The fact that traditional routing methods often overlook energy consumption, leading to unnecessary costs and environmental impacts, has led to the development of smart WDNs. This study aims to enhance energy efficiency in these networks by employing the ACO algorithm. The proposed method will focus on finding optimal routing paths for water distribution that minimize energy usage while maintaining service quality [28]. To develop a routing algorithm tailored to the specific characteristics of smart WDNs based on the ACO approach. To evaluate the energy efficiency of the proposed routing strategy compared to traditional methods. To assess the performance of the ACO algorithm under different network configurations and demand scenarios. To investigate the scalability of the ACO algorithm for large-scale WDNs.

I. Energy savings: The proposed algorithm is expected to reduce energy consumption in the smart WDN by optimizing the routing of water [25].

II. Improvement in network performance: The optimal routing paths will improve the network's overall performance by reducing congestion and increasing the reliability of water supply [26].

III. Comparison with other methods: The proposed algorithm will be compared with other optimization techniques to demonstrate its effectiveness [31].

This study investigated the application of the Ant Colony Optimization algorithm for energy-efficient routing in smart water distribution networks. Simulation results show that the proposed approach effectively minimizes energy consumption while preserving network reliability and service quality under varying demand scenarios. Compared with conventional routing methods, the ACO-based model demonstrates superior adaptability and robustness in handling complex network configurations. These findings highlight the potential of swarm intelligence techniques as practical decision-support tools for sustainable and energy-aware water management in smart city infrastructures.

A. M.: Writing-original draft, Formal analysis, and Data curation, and Conceptualization, Methodology, Visualization. A. M..: Research design, Investigation, Validation, and Supervision. The authors have read and agreed to the published version of the manuscript.

The authors consent to the publication of this manuscript.

This study does not involve any research conducted on human participants or animals.

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

All data are included in the text.

The authors declare no conflict of interest.