Solving the water distribution problem with genetic algorithm and particle swarm optimization algorithms

Water is one of the most important natural resources needed by all living things on Earth. Looking back at world history, it is evident that humanity has settled in areas close to water sources. Over time, population growth has increased water demand. Due to various reasons, clean water sources have been steadily decreasing, and water has had to be sourced from distant locations due to water shortages around settlements. In this context, the importance of water distribution networks becomes evident, as their proper design and optimal length ensure that water is delivered to settlements with minimal waste. Here, the concept of cost is also a significant factor. Designing a network with the lowest possible cost has become an important optimization problem. The operational costs of water distribution are another important concept, and considering them alongside initial investment costs further increases the need for optimization. In the design of water distribution networks, personal experience and knowledge of the geography are important, but today, with the increasing demand for water and decreasing water resources, coupled with many technological advances such as global positioning systems, the problem of water distribution remains important. Global warming is another concept that must be considered in relation to this issue. Over time, the number of scientific studies on this topic has increased, and various methods have been developed and utilized. This study aims to optimize the water distribution network. Solutions were generated using genetic algorithms and particle swarm optimization algorithms, which are heuristic algorithms. The study focuses specifically on optimizing the water distribution network in the province of Ankara. The aim is to distribute water in accordance with demand. Within the scope of the study, the mathematical model of the problem was first presented, followed by the genetic algorithm and particle swarm optimization algorithm solutions. The problem solution was coded in the Python programming language. The results obtained show that both algorithms perform similarly and provide reliable outputs regarding their impact on water consumption in districts.