GA-ILP Method for Optimization of Water Distribution Networks

Abstract

Optimization of water distribution networks has been of central importance for recent decades. Genetic Algorithms (GA) are the most famous metaheuristics widely used for this purpose with great success. However, the fact that GA basically requires a large number of computations, has led to investigate for faster solvers. In this research, a new approach is proposed in which a simple GA is linked with the Integer-Linear Programming (ILP) method resulting in a hybrid optimization scheme. Using the mathematical method of ILP, the search space is significantly reduced thereby a limited number of evaluations are required to achieve a good solution. The approach is applied to two benchmark pipe-networks in order to show its ability in terms of accuracy and speed. The results are then compared with the previous works. The obtained results indicate that the proposed model is computationally efficient, like classic methods, while is still very promising in finding the global optimum like the nature-inspired metaheuristics.

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

“…A methodology for comparing the performance of various single-objective algorithms involves assessing the best solution obtained (which is straightforward Over the past decade, an increase in the use of deterministic and hybrid methods (i.e., a combined deterministic and stochastic method) can be observed from Figure 4. These methods, which are computationally more efficient when comparing to stochastic methods, thus more suitable for large real-world applications, include ILP [51,134], MINLP [147], a combined GA and LP method (GA-LP/GALP) [113,117], combined GA and ILP method (GA-ILP) [178], combined binary LP and DE method (BLP-DE) [179], combined NLP and DE method (NLP-DE) [111], hybrid discrete dynamically dimensioned search (HD-DDS) [180], decomposition-based heuristic [52], optimal power use surface (OPUS) method paired with metaheuristic algorithms [47], and modified central force optimisation algorithm (CFOnet) [181]. However, WDS simulations may still be computationally prohibitive even with more efficient deterministic or hybrid optimisation methods, especially as the fidelity of the model and the number of decision variables increase [22].…”

Coupled Binary Linear Programming–Differential Evolution Algorithm Approach for Water Distribution System Optimization

“…It was reported by Zheng et al (2011b) that the combined NLP-DE method was able to find good quality solutions for the WDSs with great efficiency based on four case studies. Haghighi et al (2011) combined a simple GA with BLP for WDS optimization design.…”

“…In the GA-BLP method (Haghighi et al 2011), the GA was only used to deal with the NL pipes, while BLP was employed to tackle the optimization of the tree that was obtained by removing NL pipes. Thus, efficiency of the GA optimization is expected to be improved as the GA only handles the NL pipes rather than the total number pipes in the original whole network (NL is normally significantly smaller than the total number pipes).…”

Simulation-optimization Model for Design of Water Distribution System using Ant Based Algorithms

“…In general, the available methods can be classified as 1) Linear programming (LP), 2) Nonlinear programming (NLP), 3) Dynamic programming (DP) and 4) Evolutionary Algorithm (EA) such as Genetic Algorithm (GA), Simulated Annealing (SA), Shuffled Frog Leaping Algorithm (SFLA), Tabu Search (TS), Ant Colony Optimization Algorithm (ACOA), Harmony Search (HS), Particle Swarm Optimization (PSO), Cross Entropy (CE), Honey-Bee Mating Optimization (HBMO) and Differential Evolution (DE) in which they were usually used for optimal design of WDS. Haghighi et al (2011) listed different methods applied to design of WDS such as enumeration (Gessler 1985;LP (Bai et al 2007; NLP Xu and Goulter 1999;DP Schaake and Lai 1969;GA Jian and Yanbing 2010;Bi et al 2015;SA Costa et al 2000;SFLA Baoyu et al 2011;TS Lippai et al 1999;ACOA Afshar 2007;HS Yang et al 2012); PSO Babu and Vijayalakshmi 2013; HBMO Mohan and Babu 2010;CE Shibua and Janga Reddya 2012;DE Dong et al 2012 and other EA and hybrid methods Zhou et al 2016;Sheikholeslami et al 2016) in which some of them are presented in this paper. Each of these optimization methods has its own limitations.…”