A combined NLP‐differential evolution algorithm approach for the optimization of looped water distribution systems

J. Water Resour. Plann. Manage.

2014

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A coupled binary linear programming-differential evolution (BLP-DE) approach is proposed in this paper to optimize the design of water distribution systems (WDSs). Three stages are involved in the proposed BLP-DE optimization method. In the first stage, the WDS that is being optimized is decomposed into trees and the core using a graph algorithm. Binary linear programming (BLP) is then used to optimize the design of the trees during the second stage. In the third stage, a differential evolution (DE) algorithm is utilized to deal with the core design while incorporating the optimal solutions for the trees obtained in the second stage, thereby yielding near-optimal solutions for the original whole WDS. The proposed method takes advantage of both BLP and DE algorithms: BLP is capable of providing global optimal solution for the trees (no loops involved) with great efficiency, while a DE is able to efficiently generate good quality solutions for the core (loops involved) with a reduced search space compared to the original full network. Two benchmark WDS case studies and one real-world case study (with multiple demand loading cases) with a number of decision variables ranging from 21 to 96 are used to verify the effectiveness of the proposed BLP-DE optimization

Section: N O T C O P Y E D I T E Dmentioning

confidence: 99%

“…2 The solution is zero in cost as no pipe needs to be duplicated when the H R is greater than 280.8 ft and hence these solutions are not given in Table 1. Zheng et al (2011b). 3 Bolognesi et al (2010).…”

Section: N O T C O P Y E D I T E Dmentioning

confidence: 99%

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

J. Water Resour. Plann. Manage.

2014

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“…The description of the standard DE algorithm (SDE) for WDS design was introduced by Zheng et al (2011a) and hence is not repeated in this paper. Karaboga and Ökdem (2004) proposed a dither DE (DDE) where the value of mutation weighting factor (F) was randomized in a given range rather than specified a fixed value.…”

Section: Differential Evolution Algorithm Variantsmentioning

confidence: 99%

“…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. Zheng et al (2011b) investigated the sensitivity of the control parameters of DE algorithm in terms of optimizing WDSs.…”

Section: Introductionmentioning

confidence: 99%

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

World Environmental and Water Resources Congress 2012

2012

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Abstract:The differential evolution (DE) algorithm has been received some attention recently in terms of water distribution system (WDS) optimization. The DE is potentially becoming an alternative optimization tool for WDS design due to its satisfactory search performance. This paper presents a systematic performance comparison between the DE algorithm and the frequently used genetic algorithms (GAs). Two DE variants and two GA variants are compared in this paper in terms of optimizing the design of WDSs. These include the traditional DE, the dither DE algorithm, the traditional GA and the creeping mutation GA. Two well-known benchmark water distribution case studies are used in this study, which are the New York Tunnels Problem and the Hanoi Problem. The results show that the DE variants significantly outperform the GA variants in terms of both the solution quality and efficiency.

“…A spanning tree identification algorithm was introduced in their work. Zheng et al [2011] proposed a combined NLP-DE algorithm to optimize WDNs. In this algorithm, a graph theory algorithm was first used to identify the shortest-distance tree for the original whole WDN.…”

Section: Introductionmentioning

confidence: 99%

A graph decomposition‐based approach for water distribution network optimization

Water Resources Research

et al. 2013

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[1] A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.