Abstract
Genetic algorithms (GAs) have become the preferred water system design optimisation technique for many researchers and practitioners. The main reason for using GAs is their ability to deal with nonlinear complex optimisation problems. The optimal decision in terms of designing, expansion / extending, addition or rehabilitation of water supply systems has to review possible options and select a cost-effective and efficient solution. This paper presents a new approach in determining a penalty value depending on the degree of failure, of the set pressure criteria, and the importance of the link supplying a specific node. Further modifications are also made in the cross-over and mutation procedures to ensure an increase in algorithm convergence. EPANET, a widely used water distribution network simulation model, is used in conjunction with the proposed newly developed GA for the optimisation of water distribution systems. The developed GA procedure has been incorporated in a software package called GANEO, which can be used to design new networks, analyse existing networks and prioritise improvements on existing networks. The developed GA has been tested on several international benchmark problems and has proved to be very efficient and robust. The EPANET hydraulic modelling software as well as the developed GANEO software, which performs the optimisation of the water distribution network, is freeware. The software provides a tool for consulting engineers to optimise the design or rehabilitation of a water distribution network.
Highlights
As a vital part of water supply systems, water distribution networks represent one of the largest infrastructure assets of industrial society
The optimum solution of 419 000 cost units is obtained if the pipes as listed in Table 16 are used resulting in a minimum pressure in the system at node 6 being 30.44 m. These results were obtained with the following Genetic algorithms (GAs) parameter: number of generations = 1 000, population = 100, penalty factor = 1.5 and mutation rate was set at 10%
The developed genetic algorithm optimisation model was tested on three benchmark networks and it has been shown to produce good results in a limited number of generations
Summary
As a vital part of water supply systems, water distribution networks represent one of the largest infrastructure assets of industrial society. The discrete nature of the network optimisation problem (pipe diameters) and the size of the solution space make it virtually impossible to apply any of the conventional optimisation techniques to find the global optimum. GAs are applicable when a large solution space has to be searched, consisting of discreet variables, and it is accepted by most experts that the GA is the best technique for network optimisation (Van Vuuren et al, 2005). The difficulty of optimising water distribution systems is mainly due to the discrete nature of the variables and the size of the solution space. The size of the solution space (the total number of possible solutions to the problem) for the network optimisation problem can be calculated as the number of possible discrete pipe diameters to the power of the number of pipes in the network. The distribution of water through the network is governed by complex, non-linear, non-convex and discontinuous hydraulic equations (Keedwell and Khu, 2005)
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