Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization

Zecchin, Aaron C.; Simpson, Angus R.; Maier, Holger R.; Nixon, John B.

doi:10.1109/tevc.2005.844168

Cited by 169 publications

(120 citation statements)

References 29 publications

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“…Four WDS case studies have been used to investigate the effectiveness of the proposed algorithm. These include the New York Tunnels Problem (NYTP) (Dandy et al 1996), the Hanoi Problem (HP) (Fujiwara and Khang 1990), the Double New York Tunnels Problem (NYTP2) (Zecchin et al 2005) For the computer runs presented in this research the Tol value was set to be 10 -6 .…”

Section: Case Studiesmentioning

confidence: 99%

“…The current best known solutions for the NYTP, HP and NYTP2 case studies were first reported by Maier et al (2003), Reca and Martínez (2006) and Zecchin et al (2005) with costs of $38.64 million, $6.081 million and $77.28 million respectively. These current best known solutions were also found by the proposed SADE with different population sizes.…”

Section: Populatuion Size Studymentioning

confidence: 99%

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Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization

Zheng

Zecchin

Simpson

2013

J. Comput. Civ. Eng.

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Differential evolution (DE) is a relatively new technique that has recently been used to optimize the design for water distribution systems (WDSs). Several parameters need to be determined in the use of DE, including: population size, N; mutation weighting factor, F; crossover rate, CR and a particular mutation strategy. It has been demonstrated that the search behavior of DE is especially sensitive to the F and CR values. These parameters need to be fine-tuned for different optimization problems as they are generally problem-dependent. A self-adaptive differential evolution (SADE) algorithm is proposed to optimize the design of WDSs. Three new contributions are included in the proposed SADE algorithm: (i) instead of pre-specification, the control parameters of F and CR are encoded into the chromosome of the SADE algorithm and hence are adapted by means of evolution; (ii) F and CR values of the SADE algorithm apply at the individual level rather than the generational level normally used by the traditional DE algorithm; and (iii) a new convergence criterion is proposed for the SADE algorithm as the termination condition, thereby avoiding pre-specifying a fixed number of generations or computational budget to terminate the evolution. Four WDS case studies have been used to demonstrate the effectiveness of the proposed SADE algorithm. The results obtained show that the proposed algorithm exhibits good performance in terms of solution quality and efficiency. The advantage of the proposed SADE algorithm is that it reduces the effort required to fine-tune algorithm parameter values.

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Section: Case Studiesmentioning

confidence: 99%

Section: Populatuion Size Studymentioning

confidence: 99%

Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization

Zheng

Zecchin

Simpson

2013

J. Comput. Civ. Eng.

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“…It employs some basic heuristics in order to escape from local optima and to find the global optimum in a multimodal space. The most successful applications of ant algorithms are combinatorial optimization and network problems, such as the traveling salesman problem (TSP) [4] , the vehicle routing problem (VRP) [5,6] , the job shop scheduling problem (JSP) [7] , the water distribution system (WDS) [8] , data mining [9] and network routing [10] , etc. However, all these algorithms are designed for solving discrete optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Zhang

2008

J. Comput. Sci. Technol.

103

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Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

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“…Varios autores han utilizado, dentro del proceso de optimización, una función objetivo que sólo considera los costes de las tuberías (Maier et al, 2003;Zecchin et al, 2005). Otros han tomado en cuenta varios costes involucrados (Matías, 2003;Dandy y Engelhardt, 2006).…”

Section: La Función Objetivounclassified

Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

Arango¹

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Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization

Cited by 169 publications

References 29 publications

Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization

Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

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