Differential evolution algorithm for optimal design of water distribution networks

Abstract

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.

Optimization of the Water Distribution Networks with Differential Evolution (DE) and Mixed Integer Linear Programming (MILP)

“…The most optimal answers for coefficients are 0.6 and 0.5 for F and Cr coefficients, respectively. These values matched with the results of Suribabu [33].…”

“…According the literature review in the differential evolution algorithm (Suribabu, [33]) and other evolutionary algorithms, to find the best conditions for optimizing water distribution network, at first considering an initial population of 100 member (N = 100) and generation of 500 (G = 500) to find the coefficients of F and CR, 18 different combinations of these factors was examined. It should be mentioned, at study each of the condition in this algorithm, three runs were conducted and the optimal run was chosen for that.…”

A graph decomposition‐based approach for water distribution network optimization

“…These include linear programming (LP) [Alperovits and Shamir, 1977], nonlinear programming (NLP) [Fujiwara and Khang, 1990], and evolutionary algorithms (EAs) [Dandy et al, 1996;Montesinos et al, 1999;Reca and Mart ınez, 2006;Maier et al, 2003;Tolson et al, 2009;Suribabu, 2010;Zheng et al, 2013a]. However, it has been found that each optimization algorithm has its own advantages and disadvantages.…”

A Performance Comparison of Differential Evolution and Genetic Algorithm Variants Applied to Water Distribution System Optimization

“…In addition to these three parameters, a particular mutation strategy needs to be selected for the use of DE among a number of availabilities (Price et al 2005). Vasan and Simonovic (2010) and Suribabu (2010) applied DE to the optimization of WDSs, and concluded that the DE was able to find the optimal solutions with great efficiency. Zheng et al (2011a) developed a DE combined with the NLP method for optimizing WDS design.…”

“…However, this conclusion cannot necessarily be directly transferred to the WDS optimization since the search space landscape of numerical optimization problems differs significantly to that of the WDS optimization problem. Suribabu (2010) concluded that the DE has at least the same, if not better, performance than GAs in terms of WDS optimization. In contrast, Dandy et al (2011) reported that GAs had overall better performance than the DE in terms of solution quality and efficiency based on testing for two WDS case studies.…”