Application of two ant colony optimisation algorithms to water distribution system optimisation

Zecchin, Aaron C.; Simpson, Angus R.; Maier, Holger R.; Leonard, Michael; Roberts, Andrew J.; Berrisford, M.

doi:10.1016/j.mcm.2006.01.005

Cited by 151 publications

(67 citation statements)

References 20 publications

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“…Comparison of the results shows that ACOA2 has been able to outperform the other used methods with no extra computational time cost due to the unique feature of this method which is presented before. In addition, the near optimal solution of Maier et al (2003), Zecchin et al (2006) and Afshar (2007), 38.64 (M$), is obtained here using proposed ACOA2 with smaller computational time.…”

Section: Define the Problem Graph Start From Arbitrary Nodementioning

confidence: 78%

Simulation-optimization Model for Design of Water Distribution System using Ant Based Algorithms

Moeini

S.A.M.²

2018

Jou. Eng. Res.

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Abstract:In this paper, the Ant Colony Optimization Algorithm (ACOA) is applied to solve Water Distribution System design optimization problem proposing two different methods. Considering pipe diameters as decision variables of the problem, Ant System and Max-Min Ant System, referred to ACOA1 and ACOA2 respectively, are applied to determine pipe diameters. In proposed methods, the ant-based models are interfaced with EPANET as simulator for the hydraulic analysis. Three benchmark test examples are solved with proposed methods and the results are presented and compared with those obtained with other existing methods. The results show the capability of the proposed methods to optimally solve the design optimization problem in which best results are obtained with ACOA2 in comparison with other available results. Furthermore, the results show the superiority of the proposed ACOA2 over than the ACOA1 in which the trade-off between the two contradictory search characteristic of exploration and exploitation is managed better by using MaxMin Ant System. Keywords

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Section: Define the Problem Graph Start From Arbitrary Nodementioning

confidence: 78%

Simulation-optimization Model for Design of Water Distribution System using Ant Based Algorithms

Moeini

S.A.M.²

2018

Jou. Eng. Res.

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“…There are many heuristic algorithms that are developed in recent years, such as Genetic Algorithms [17][18][19][20][21][22][23], Particle Swarm Optimization [24,25], Shuffled Complex Evolution (SCE) [26,27] and Ant Colony Optimization [28][29][30]. The SCE algorithm is one of the popular optimization algorithms in the river basin model calibration over the past 10 years given that more than 300 different publications referenced the original SCE publications [26,27,31].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

Chu

Peng

Ding

et al. 2015

Water

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River basin simulation and multi-reservoir optimal operation have been critical for river basin management. Due to the intense interaction between human activities and river basin systems, the river basin model and multi-reservoir operation model are complicated with a large number of parameters. Therefore, fast and stable optimization algorithms are required for river basin management under the changing conditions of climate and current human activities. This study presents a new global optimization algorithm, named as heuristic dynamically dimensioned search with sensitivity information (HDDS-S), to effectively perform river basin simulation and multi-reservoir optimal operation during river basin management. The HDDS-S algorithm is built on the dynamically dimensioned search (DDS) algorithm; and has an improved computational efficiency while maintaining its search capacity compared to the original DDS algorithm. This is mainly due to the non-uniform probability assigned to each decision variable on the basis of its changing sensitivity to the optimization objectives during the adaptive change from global to local search with dimensionality reduced. This study evaluates the new algorithm by comparing its performance with the DDS algorithm on a river basin model calibration problem and a multi-reservoir optimal operation problem. The results obtained indicate that the HDDS-S algorithm outperforms the DDS algorithm in terms of search ability and computational efficiency in the two specific problems. In addition; similar to the DDS algorithm; the HDDS-S algorithm is easy to use as it does not require any parameter OPEN ACCESSWater 2015, 7 2215 tuning and automatically adjusts its search to find good solutions given an available computational budget.

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“…Durante la última década, muchos investigadores han empezado a hacer uso de modernas técnicas evolutivas de optimización, dejando atrás otros métodos más tradicionales basados en la programación lineal y no lineal. Refiriéndonos exclusivamente al campo del agua, los algoritmos genéticos han sido los más utilizados (Savic y Walters, 1997;Wu y Simpson, 2001;Matías, 2003;Wu y Walski, 2005), aunque también han sido incorporadas otras técnicas, como las basadas en las colonias de hormigas (ACO, Ant Colony Optimization) (Zecchin et al, 2006;Montalvo et al, 2007a); Simulated Annealing, también denominada 'recocido simulado' (Cunha y Sousa, 1999); Shuffled Complex Evolution (Liong y Atiquzzama, 2004); Harmony Search o búsqueda de la armonía (Geem, 2006); Particle Swarm Optimization (PSO), basada en la inteligencia colectiva de los sistemas de partículas, Montalvo et al, 2008e). Entre las ventajas que han propiciado el uso creciente de los algoritmos evolutivos en el diseño óptimo de SDA, pueden citarse las siguientes:…”

Section: Comentariosunclassified

“…During the last decade, many researchers have started using modern evolutionary optimisation techniques, leaving aside more traditional methods based on linear and nonlinear programming. In the field of water systems, genetic algorithms have been the most used (Savic y Walters, 1997;Wu y Simpson, 2001;Matías, 2003;Wu y Walski, 2005), although other techniques based on ant colonies (ant colony optimisation or ACO) (Zecchin et al, 2006;Montalvo et al, 2007a) have been used; as well as simulated annealing (Cunha y Sousa, 1999); shuffled complex evolution (Liong y Atiquzzama, 2004); harmony search (Geem, 2006); and particle swarm optimisation (PSO) based on the collective intelligence of systems of particles Montalvo et al, 2008e). The advantages of the growing use of evolutionary algorithms in the optimal design of WDS include:…”

Section: Observationsmentioning

confidence: 99%

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

Arango¹

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Application of two ant colony optimisation algorithms to water distribution system optimisation

Abstract: Voluntary posting on open web sites operated by author or author's institution for scholarly purposes.

Cited by 151 publications

References 20 publications

Simulation-optimization Model for Design of Water Distribution System using Ant Based Algorithms

Simulation-optimization Model for Design of Water Distribution System using Ant Based Algorithms

A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

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

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