A hybrid genetic algorithm for the design of water distribution networks

Genetic algorithms are currently one of the state-of-the-art techniques for the optimisation of engineering systems including water network design and rehabilitation. They are capable of finding near optimal cost solutions to these problems given certain cost and hydraulic parameters. However, many forms of genetic algorithms rely on random starting points that are often poor solutions and the problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a novel method, known as CANDA-GA, which uses a heuristic-based, local representative cellular automata approach to provide a good initial population for genetic algorithm runs. CANDA-GA is applied to three networks, one taken from the literature and two taken from industry. The results show that the proposed method consistently outperforms the conventional non-heuristic-based GA approach in terms of producing more economically designed water distribution networks.