Application of particle swarm optimization (PSO) is demonstrated through design of a water distribution pipeline network. PSO is an evolutionary algorithm that utilizes the swarm intelligence to achieve the goal of optimizing a specified objective function. This algorithm uses the cognition of individuals and social behaviour in the optimization process. For the optimization of water distribution system, a simulation – optimization model, called PSONET is developed and used in which the optimization is by PSO. This formulation is applied to two benchmark optimization design problems. The results are compared with the results obtained by other optimization methods. The results show that the PSO is more efficient than other optimization methods as it requires fewer objective function evaluations.

Water distribution networks are considered as the most important entity in the urban infrastructure system and need huge investment for construction. The inherent problem associated with cost optimisation in the design of water distribution networks is due to the nonlinear relationship between flow and head loss and availability of the discrete nature of pipe sizes. In the last few decades, many researchers focused on several stochastic methods of optimisation algorithms. The present paper is focused on the Differential Evolution algorithm (henceforth referred to as DE) and utilises a similar concept as the genetic algorithm to achieve a goal of optimisation of the specified objective function. A simulation–optimisation model is developed in which the optimization is done by DE. Four well-known benchmark networks were taken for application of the DE algorithm to optimise pipe size and rehabilitation of the water distribution network. The findings of the present study reveal that DE is a good alternative to the genetic algorithm and other heuristic approaches for optimal sizing of water distribution pipes.

An economic design of water distribution system components plays an important role in the water-supplying agency because of the requirement of a large financial investment on construction, operation, and maintenance. Population-based stochastic search algorithms have become a futuristic method for optimal design of water distribution networks. However, the effectiveness of population-based stochastic search algorithms purely depends on several factors, such as population size, degree of randomness involved, penalty function, algorithm operators, and search space size. An analysis of published research shows that a large number of evaluations is required to arrive at the least cost combination of pipe diameters. This paper proposes a new approach, in which flow velocity in a pipe is considered as implicit information for pipe sizing. Three well-known benchmark networks were used to illustrate the applicability of the proposed approach for least-cost pipe sizing of water distribution networks. The result of the model application reveals that the computational effort required to arrive at an economical design is very minimal.