Optimal pipe dimensioning in water distribution networks using Gravitational Search Algorithm. (2nd November 2021)

• Record Type: Journal Article
• Authors: Fallah, Hossein; Ghazanfari, Sadegh; Suribabu, C.R.; Rashedi, Esmat
• Is Part Of: ISH journal of hydraulic engineering. Volume 27(2021)supplement 1
• Page Start: 242
• Page End: 255
• Publication Date: 2021-11-02
• Subjects: Evolutionary Algorithms (EA) -- Gravitational Search Algorithm (GSA) -- Meta-heuristic Algorithms (MHA) -- Optimization -- Water Distribution Network (WDN); Hydraulic engineering -- Periodicals; Hydraulic engineering -- India -- Periodicals; Hydraulic engineering; India; Periodicals; 627
• DOI: 10.1080/09715010.2019.1624630
• Languages: English
• ISSNs: 0971-5010

Abstract

ABSTRACT: An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.