Optimal Design of Water Networks Using a Modified Genetic Algorithm with Reduction in Search Space

Kadu, M. S.; Gupta, Rajesh; Bhave, Pramod R.

doi:10.1061/(asce)0733-9496(2008)134:2(147)

Cited by 171 publications

(118 citation statements)

References 23 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…Examples of methods based on changes to the search algorithm (using heuristic information/knowledge) include those that use some knowledge of the problem under consideration to modify the underlying search methodology. For example, Kadu et al (2008), Zheng et al (2011) andZheng et al (2013a) used critical/shortest path methods to reduce the search space for the optimal design of water distribution networks.…”

Section: Current Statusmentioning

confidence: 99%

“…While the advantages of having a universally applicable methodology are obvious, the price to pay is often the loss of computational efficiency or poor quality results when applied to a problem with a large search space. For example, Guo et al (2007), Kadu et al (2008), Keedwell and Khu (2005) and Zheng et al (2011Zheng et al ( , 2013a demonstrate that carefully structured problem specific EA implementations outperform their generic counterparts. From this standpoint, an important question to be answered is: Can more generic methods be developed (e.g.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

532

317

View full text Add to dashboard Cite

In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

show abstract

Section: Current Statusmentioning

confidence: 99%

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

532

317

View full text Add to dashboard Cite

show abstract

“…All the pipes have an identical Hazen-Williams coefficient of 130. A total of 14 commercially available pipe diameters ranging from 150 mm up to 1000 mm are available for selection for each pipe and the cost of each pipe is given by Kadu et al (2008).…”

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

Zheng

Simpson

Zecchin

2014

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

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

show abstract

“…Much work has been done in the literature on the application of genetic algorithms to the problem of optimal design of water distribution systems (Simpson et al, 1994;Prasad & Park, 2004;Kadu et al, 2008). Genetic algorithms have several advantages over other mathematical optimisation techniques (Goldberg, 1989).…”

Section: Optimisation Techniquesmentioning

confidence: 99%

Optimal Reliability-Based Design of Bulk Water Supply Systems

Chang

Zyl

2014

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

DJ :;sc rt(l 1I UIl s uhnlltt ed In p a rt i~l fulfi lm en t o~' th e reqlllrcrnen ts fu r tht., degree ofM~s t e r ofSClence In Enginee ring AbstractBulk water supply systems are usually designed according to deterministic design guidelines. In South Africa, design guidelines specify that a bulk storage reservoir should have a storage capacity of 48 hours of annual average daily demand (AADD), and the feeder pipe a capacity of 1.5 times AADD (CSIR, 2000). Nel & Haarhoff (1996) proposed a stochastic analysis method that allowed the reliability of a reservoir to be estimated based on a Monte Carlo analysis of consumer demand, fire water demand and pipe failures. Van Zyl et al. (2008) developed this method further and proposed a design criterion of one failure in ten years under seasonal peak conditions.In this study, a method for the optimal design of bulk water supply systems is proposed with the design variables being the configuration of the feeder pipe system, the feeder pipe diameters (i.e. capacity), and the size of the bulk storage reservoir. The stochastic analysis method is applied to determine a trade-off curve between system cost and reliability, from which the designer can select a suitable solution.Optimisation of the bulk system was performed using the multi-objective genetic algorithm, NSGA-II. As Monte Carlo sampling can be computationally expensive, especially when large numbers of simulations are required in an optimisation exercise, a compression heuristic was implemented and refined to reduce the computational effort required of the stochastic simulation. Use of the compression heuristic instead of full Monte Carlo simulation in the reliability analysis achieved computational time savings of around 75% for the optimisation of a typical system.Application of the optimisation model showed that it was able to successfully produce a set of Pareto-optimal solutions ranging from low reliability, low cost solutions to high reliability, high cost solutions. The proposed method was first applied to a typical system, resulting in an optimal reservoir size of approximately 22 h AADD and feeder pipe capacity of 2 times AADD. This solution achieved 9% savings in total system cost compared to the South African design guidelines. In addition, the optimal solution proved to have better reliability that one designed according to South African guidelines.A sensitivity analysis demonstrated the effects of changing various system and stochastic parameters from typical to low and high values. The sensitivity results revealed that the length of the feeder pipe system has the greatest impact on both the cost and reliability of the bulk system. It was also found that a single feeder pipe is optimal in most cases, and that parallel feeder pipes are only optimal for short feeder pipe lengths.The optimisation model is capable of narrowing down the search region to a handful of possible design solutions, and can thus be used by the engineer as a tool to assist with the design of the final system.

show abstract

Optimal Design of Water Networks Using a Modified Genetic Algorithm with Reduction in Search Space

Cited by 171 publications

References 23 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

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

Optimal Reliability-Based Design of Bulk Water Supply Systems

Contact Info

Product

Resources

About