Hydraulic Infrastructures Design Using Simulated Annealing

Cunha, Maria da Conceição; Sousa, Joaquim

doi:10.1061/(asce)1076-0342(2001)7:1(32)

Cited by 74 publications

(32 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The data used here can be found in Cunha and Sousa (1999) for networks 1 and 2, in Cunha and Sousa (2001) for network 3, and in Sousa and Cunha (1999) for networks 4 and 5. Network 4 has been solved for two different minimum diameter constraints (D min ¼ 2 and 4 in.).…”

Section: Algorithm Implementation and Resultsmentioning

confidence: 99%

“…Realistic designs need discrete diameter solutions. The diameters corresponding to networks 1 and 2 are the same as those presented in Cunha and Sousa (1999), and the diameters corresponding to network 3 are the same as those presented in Cunha and Sousa (2001). The diameters for networks 4 and 5 are shown in Tables 2 and 3.…”

Section: Termination Criteriamentioning

confidence: 99%

“…Among them we find genetic algorithms (used by Goldberg and Kuo, 1987;Hadji and Murphy, 1990;Murphy and Simpson, 1992;Walters and Cembrowicz, 1993;Simpson et al, 1994;Savic and Walters, 1997;Walters et al, 1999), and simulated annealing (used by Cunha and Sousa, 1999;Cunha and Sousa, 2001). Theoretical proofs relative to the convergence of simulated annealing with global optimal solutions have been formulated, but it would be impractical to observe theoretical conditions in real-world applications .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tabu search algorithms for water network optimization

Cunha

Ribeiro²

2004

European Journal of Operational Research

119

View full text Add to dashboard Cite

In this paper we propose a tabu search algorithm to find the least-cost design of looped water distribution networks. The mathematical nature of this optimization problem, a nonlinear mixed integer problem, is at the origin of a multitude of contributions to the literature in the last 25 years. In fact, exact optimization methods have not been found for this type of problem, and, in the past, classical optimization methods, like linear and nonlinear programming, were tried at the cost of drastic simplifications. Tabu search is a valuable heuristic technique for solving problems cast in combinatorial form. This is based on the human memory process and uses an iterative neighborhood search procedure in an attempt to avoid becoming trapped in local optima. The use of such a heuristic procedure to solve the aforementioned problem needs particular tailoring to produce high quality solutions. In this paper we present the essential features of the algorithm and the results obtained when it is applied to some of the classical water distribution network case studies appearing in the literature. The results are very promising and demonstrate the usefulness of tabu search algorithms in solving this kind of optimization problem.

show abstract

Section: Algorithm Implementation and Resultsmentioning

confidence: 99%

Section: Termination Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tabu search algorithms for water network optimization

Cunha

Ribeiro²

2004

European Journal of Operational Research

119

View full text Add to dashboard Cite

show abstract

“…Afterwards, a number of other evolutionary algorithms were developed and applied to WDS design. These include simulated annealing (Cunha and Sousa 2001); harmony search (Geem et al 2002); the shuffled frog leaping algorithm (Eusuff and Lansey 2003); Ant Colony Optimization (Maier et al M a n u s c r i p t N o t C o p y e d i t e d (Suribabu and Neelakantan 2006); cross entropy (Perelman and Ostfeld, 2007); scatter search (Lin et al 2007); HD-DDS (Tolson et al 2009) and differential evolution (Suribabu 2010). These EAs have been applied to a number of WDS case studies and exhibit good performance in terms of finding optimal solutions.…”

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

“…Over the last two decades, a number of EAs have been employed to optimize the design of WDSs. Murphy and Simpson (1992) were first to introduce genetic algorithms (GAs) for water network optimization; Cunha and Sousa (2001) used simulated annealing to optimize WDSs; Geem et al (2002) developed a harmony search model for optimizing WDSs; Eusuff and Lansey (2003) proposed a shuffled frog leaping algorithm (SFLA) for network optimization; and Maier et al (2003) applied an Ant Colony Optimization approach to optimize WDSs. More recently, Tolson et al (2009) developed a new algorithm (the hybrid discrete dynamically dimensioned search algorithm HD-DDS) to optimize WDSs.…”

Section: Introductionmentioning

confidence: 99%

Performance Study of Differential Evolution with Various Mutation Strategies Applied to Water Distribution System Optimization

Zheng

Simpson

Zecchin

2011

World Environmental and Water Resources Congress 2011

View full text Add to dashboard Cite

Differential evolution (DE) is a relatively new optimization technique that has been employed to optimize the design of water distribution systems (WDSs). There are three important operators involved in the use of DE: mutation, crossover and selection. These operators are similar to the commonly used genetic algorithms (GAs). However, DE differs significantly from GAs in that mutation is an important operator for DE, while in contrast, crossover is an important operator for GAs. It has been found that the success of the DE algorithm in solving different mathematical optimization problems crucially depends on the mutation strategy that is used. This paper aims to investigate the relative effectiveness of five frequently used mutation strategies of DE when applied to WDS optimization. The five DE variants with different mutation strategies are applied to two well-known WDS case studies: the New York Tunnels Problem and the Hanoi Problem.

show abstract

Hydraulic Infrastructures Design Using Simulated Annealing

Cited by 74 publications

References 29 publications

Tabu search algorithms for water network optimization

Tabu search algorithms for water network optimization

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

Performance Study of Differential Evolution with Various Mutation Strategies Applied to Water Distribution System Optimization

Contact Info

Product

Resources

About