An Improved Genetic Algorithm for Pipe Network Optimization

Dandy, Graeme C.; Simpson, Angus R.; Murphy, Laurence

doi:10.1029/95wr02917

Cited by 420 publications

(232 citation statements)

References 13 publications

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“…GA are increasingly used in a broad spectrum of engineering applications, such as pipe network design [Dandy et al, 1996 [Chavent, 1975;Townley and Wilson, 1985] imposes some specific characteristics: (1) As a unique solution is unattainable in practice, the parameter space must be widely scanned, and to avoid a premature convergence on a local minimum, a certain amount of variability should be kept within the set of potential solutions; (2) for T values the order of magnitude is as important, even more important, than the decimal accuracy. In other words, the coding of individuals must allow the adjustment with a relative easiness either of the order of magnitude or of the decimal accuracy of the transmissivity.…”

Section: Multipopulation Genetic Algorithmmentioning

confidence: 99%

A multipopulation genetic algorithm to solve the inverse problem in hydrogeology

Karpouzos¹,

Delay²,

Katsifarakis³

et al. 2001

Water Resources Research

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Abstract. The inverse problem of groundwater flow is treated with an automatic method that can produce several alternative solutions at once. During their joint optimization, these solutions can exchange information in order to maintain some diversity and thus avoid a systematic premature convergence toward a single local minimum. Although genetic algorithms are capable of doing this, they have not often been used in groundwater inverse optimization. First, a specific multipopulation genetic algorithm is developed. It is then tested on two synthetic cases of steady state flow with transmissivity values extending over 4 orders of magnitude. The first test is nonparametric and optimizes as many parameters as those used to define the reference case. The second test uses a sort of "pilot point" parametrization. The optimization is carried out on a limited number of perturbations that are interpolated and superimposed on an initial transmissivity field. In view of the good quality of the results, these initial attempts provide incentives to further develop genetic algorithms in groundwater inverse problems.

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Section: Multipopulation Genetic Algorithmmentioning

confidence: 99%

A multipopulation genetic algorithm to solve the inverse problem in hydrogeology

Karpouzos¹,

Delay²,

Katsifarakis³

et al. 2001

Water Resources Research

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“…To test it, some local optima Gessler (1982) 41.8 Discrete diameters Morgan and Goulter (1985) 38.9 Split-pipe Morgan and Goulter (1985) 39.2 Discrete diameters Goulter et al (1986) 435 Split-pipe Kessler (1988) 39.0 Split-pipe Kessler and Shamir (1989) 418 Split-pipe Fujiwara and Khang (1990) 36.6 Split-pipe Khang (1990, 1991) 6116 Continuous diameters Khang (1990, 1991) 6319 Split-pipe Walski et al (1990) 1884.432 Discrete diameters Sonak and Bhave (1993) 6045 Split-pipe Murphy et al (1993) 38.8 Discrete diameters Eiger et al (1994) 402 6027 Split-pipe Loganathan et al (1995) 38.0 Split-pipe Dandy et al (1996) 38.8 Discrete diameters Varma et al (1997) 6000 Continuous diameters Varma et al (1997) 6162 Discrete diameters Savic and Walters (1997) solutions regarding New York network (network 3) included in the literature were used as starting solutions to run the tabu search algorithm developed. They are presented in Table 5 for Morgan and Goulter (1985) and Murphy et al (1993) and in Table 6 for Gessler (1982).…”

Section: Resultsmentioning

confidence: 99%

Tabu search algorithms for water network optimization

Cunha

Ribeiro²

2004

European Journal of Operational Research

124

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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.

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“…Amongst many other types of applications, GAs have been used for water-distribution networks, primarily for design purposes (see Dandy et al 1996). They have also been used for pump scheduling (Mackle et al 1995;Savic et al 1997;Boulos et al 2001;Rao & O'Connell 2002).…”

Section: Approach Adoptedmentioning

confidence: 99%

Development of a real-time, near-optimal control process for water-distribution networks

Rao

Salomons

2007

Journal of Hydroinformatics

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This paper presents a new approach for the real-time, near-optimal control of water-distribution networks, which forms an integral part of the POWADIMA research project. The process is based on the combined use of an artificial neural network for predicting the consequences of different control settings and a genetic algorithm for selecting the best combination. By this means, it is possible to find the optimal, or at least near-optimal, pump and valve settings for the present time-step as well as those up to a selected operating horizon, taking account of the short-term demand fluctuations, the electricity tariff structure and operational constraints such as minimum delivery pressures, etc. Thereafter, the near-optimal control settings for the present time-step are implemented. Having grounded any discrepancies between the previously predicted and measured storage levels at the next update of the monitoring facilities, the whole process is repeated on a rolling basis and a new operating strategy is computed. Contingency measures for dealing with pump failures, pipe bursts, etc., have also been included. The novelty of this approach is illustrated by the application to a small, hypothetical network. Its relevance to real networks is discussed in the subsequent papers on case studies.

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An Improved Genetic Algorithm for Pipe Network Optimization

Cited by 420 publications

References 13 publications

A multipopulation genetic algorithm to solve the inverse problem in hydrogeology

A multipopulation genetic algorithm to solve the inverse problem in hydrogeology

Tabu search algorithms for water network optimization

Development of a real-time, near-optimal control process for water-distribution networks

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