Ant Colony Optimization for the Design of Water Distribution Systems

Maier, Holger R.; Simpson, Angus R.; Foong, Wai Kuan; Phang, Kuang Yeow; Seah, Hsin Yeow; Tan, Chan Lim

doi:10.1061/40569(2001)375

Cited by 94 publications

(132 citation statements)

References 16 publications

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“…Both of these methods, however, compare favourably with 97,750 evaluations required by the method of Dandy et al [9], 46,016 evalu- [19] to get the solution to this problem. It should be noted here that the method of Maier et al [19] uses two penalty parameters, one for calculating the total cost of infeasible solutions and the other for adversely penalising the amount of pheromone change calculated for these solutions. Introduction of these penalty parameters, of course, requires some preliminary runs for tuning purposes which adds to the computation requirements of the method.…”

Section: Test Problemmentioning

confidence: 86%

A parameter-free self-adapting boundary genetic search for pipe network optimization

Afshar

Mariňo

2007

Comput Optim Appl

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Commercial application of genetic algorithms (GAs) to engineering design problems, including optimal design of pipe networks, could be facilitated by the development of algorithms that require the least number of parameter tuning. This paper presents an attempt to eliminate the need for defining a priori the proper penalty parameter in GA search for pipe networks optimal designs. The method is based on the assumption that the optimal solution of a pipe network design problem lies somewhere on, or near, the boundary of the feasible region. The proposed method uses the ratio of the best feasible and infeasible designs at each generation to guide the direction of the search towards the boundary of the feasible domain by automatically adjusting the value of the penalty parameter. The value of the ratio greater than unity is interpreted as the search being performed in the feasible region and vice versa. The new adapted value of the penalty parameter at each generation is therefore calculated as the product of its current value and the aforementioned ratio. The genetic search so constructed is shown to converge to the boundary of the feasible region irrespective of the starting value of the constraint violation penalty parameter. The proposed method is described here in the context of pipe network optimisation problems but is equally applicable to any other constrained optimisation problem. The effectiveness of the method is illustrated with a benchmark pipe network optimization example from the literature.

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Section: Test Problemmentioning

confidence: 86%

A parameter-free self-adapting boundary genetic search for pipe network optimization

Afshar

Mariňo

2007

Comput Optim Appl

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“…Recently, many authors have proposed genetic algorithms for water distribution network design (Montesinos et al 1999;Vairavamoorthy and Ali 2000;Reca and Martínez 2006). Other meta-heuristic methods, simulated annealing (Cunha and Sousa 1999), harmony search method (Geem et al 2001) and the ant colony optimization method (Maier et al 2003), have also been recently applied to this problem. Reca and Martínez (2006) developed the GENOME model, a Genetic Algorithm based model.…”

Section: Notationmentioning

confidence: 99%

Application of Several Meta-Heuristic Techniques to the Optimization of Real Looped Water Distribution Networks

Reca

Martínez

Gil

et al. 2007

Water Resour Manage

109

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The optimization of looped water distribution systems is a complex problem as the pipe flows are unknown variables. Although many researchers have reported algorithms for minimizing the network cost applying a large variety of techniques, such as linear programming, non-linear programming, global optimization methods and meta-heuristic approaches, a totally satisfactory and efficient method is not available as yet. Many works have assessed the performance of these techniques using small or medium-sized benchmark networks proposed in the literature, but few of them have tested these methods with large-scale real networks. The aim of this paper is to evaluate the performance of several meta-heuristic techniques: genetic algorithms, simulated annealing, tabu search, and iterated local search. These techniques were first validated and compared by applying them to a medium-sized benchmark network previously reported in the literature. They were then applied to a large irrigation water distribution network that has been proposed in a previous work to assess their performance in a practical application. All the methods tested performed adequately well, compared with the results found in previous works. Genetic algorithm was more efficient when dealing with a medium-sized network, but other methods outperformed it when dealing with a real complex one.

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“…Readers are referred to Dorigo and Stützle (2004) for a detailed discussion of ACO metaheuristics and the benchmark combinatorial optimization problems to which ACO has been applied. Due to its robustness in solving these problems, ACO has recently been applied to, and obtained some encouraging results for, real-world engineering problems, such as the design of optimal water distribution systems (Maier et al 2003) and in the area of power systems (Huang 2001;Gomez et al 2004;Kannan et al 2005;Su et al 2005). For a metaheuristic method to be applied to any class of optimization problem, a formulation must be proposed such that a link between the adopted method and the problem to be solved is established.…”

Section: Aco Formulation For Power Plant Maintenance Scheduling Optimmentioning

confidence: 99%

Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system

et al. 2007

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Ant Colony Optimization for the Design of Water Distribution Systems

Cited by 94 publications

References 16 publications

A parameter-free self-adapting boundary genetic search for pipe network optimization

A parameter-free self-adapting boundary genetic search for pipe network optimization

Application of Several Meta-Heuristic Techniques to the Optimization of Real Looped Water Distribution Networks

Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system

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