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Journal Article•DOI•

Optimal Water Distribution Network Design with Honey-Bee Mating Optimization

S. Mohan1, K. S. Jinesh Babu1•
Indian Institute of Technology Madras1
01 Jan 2010-Journal of Computing in Civil Engineering (American Society of Civil Engineers)-Vol. 24, Iss: 1, pp 117-126
TL;DR: The importance and huge cost of water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas.
Abstract: Water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas. The importance and huge c...
Citations
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Journal Article•DOI•
Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

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Helena Mala-Jetmarova, Nargiz Sultanova, Dragan Savic
13 Mar 2018-Water
TL;DR: In this article, a review of water distribution system design is presented, which is relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution networks.
Abstract: Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

111 citations

Cites methods from "Optimal Water Distribution Network ..."

  • ...A great range of those methods has been applied to optimise design of WDSs to date, inclusive of (but not limited to) a GA [42,45,50,85,86,152–154], fmGA [88], non-crossover dither creeping mutation-based GA (CMBGA) [149], adaptive locally constrained GA (ALCO-GA) [155], SA [60], shuffled frog leaping algorithm (SFLA) [103], ACO [104,156], shuffled complex evolution (SCE) [157], harmony search (HS) [105,158,159], particle swarm HS (PSHS) [160], parameter setting free HS (PSF HS) [161], combined cuckoo-HS algorithm (CSHS) [162], particle swarm optimisation (PSO) [106,153,154], improved PSO (IPSO) [163], accelerated momentum PSO (AMPSO) [164], integer discrete PSO (IDPSO) [165], newly developed swarm-based optimisation (DSO) algorithm [150], scatter search (SS) [166], CE [61,62], immune algorithm (IA) [167], heuristic-based algorithm (HBA) [168], memetic algorithm (MA) [107], genetic heritage evolution by stochastic transmission (GHEST) [169], honey bee mating optimisation (HBMO) [170], DE [46,153,154,171], combined PSO and DE method (PSO-DE) [172], self-adaptive DE method (SADE) [173], NSGA-II [70], ES [68], NSES [92], cost gradient-based heuristic method [119], improved mine blast algorithm (IMBA) [174], discrete state transition algorithm (STA) [175], evolutionary algorithm (EA) [109], and convergence-trajectory controlled ACO (ACOCTC) [176]....

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  • ...Mohan and Babu (2010) [170] SO Optimal WDS design using honey bee mating optimisation (HBMO)....

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  • ...ACO ant colony optimisation ACOCTC convergence-trajectory controlled ant colony optimisation ACS ant colony system AEF average emissions factor AIM artificial inducement mutation ALCO-GA adaptive locally constrained genetic algorithm AMPSO accelerated momentum particle swarm optimisation ANN artificial neural network AS ant system ASelite elitist ant system ASrank elitist rank ant system BB branch and bound BB-BC big bang-big crunch BLIP binary linear integer programming BLP-DE combined binary linear programming and differential evolution BWN-II battle of the water networks II (optimisation problem) CA cellular automaton CAMOGA cellular automaton and genetic approach to multi-objective optimisation CANDA cellular automaton for network design algorithm CC chance constraints CDGA crossover dither creeping mutation genetic algorithm CE cross entropy CFO central force optimisation CGA crossover-based genetic algorithm with creeping mutation CMBGA non-crossover dither creeping mutation-based genetic algorithm CR crossover probability (parameter) CS cuckoo search Water 2018, 10, 307 27 of 103 CSHS combined cuckoo-harmony search CTM cohesive transport model D design dDE dither differential evolution DDSM demand-driven simulation method DE differential evolution DMA district metering area DPM discoloration propensity model DSO newly developed swarm-based optimisation algorithm EA evolutionary algorithm EA-WDND evolutionary algorithm for solving water distribution network design EEA embodied energy analysis EEF estimated (24-h) emissions factor (curve) EF emissions factor EPANETpdd pressure-driven demand extension of EPANET EPS extended period simulation ES evolution strategy F mutation weighting factor (parameter) FCV flow control valve fmGA fast messy genetic algorithm FSP fixed speed pump GA genetic algorithm GA-ILP combined genetic algorithm and integer linear programming GA-LP/GALP combined genetic algorithm and linear programming GANEO genetic algorithm network optimisation (program) GENOME genetic algorithm pipe network optimisation model GHEST genetic heritage evolution by stochastic transmission GHG greenhouse gas (emissions) GOF gradient of the objective function GP genetic programming GRG2 generalised reduced gradient (solver) GUI graphical user interface HBA heuristic-based algorithm HBMO honey bee mating optimisation HD-DDS hybrid discrete dynamically dimensioned search HDSM head-driven simulation method HMCR harmony memory considering rate (parameter) HMS harmony memory size (parameter) HS harmony search IA immune algorithm IDPSO integer discrete particle swarm optimisation ILP integer linear programming IMBA improved mine blast algorithm IPSO improved particle swarm optimisation KLSM Kang and Lansey’s sampling method [26] LCA life cycle analysis LHS Latin hypercube sampling LINDO linear interactive discrete optimiser LM Lagrange’s method LP linear programming LTF linear transfer function MA memetic algorithm MBA mine blast algorithm Water 2018, 10, 307 28 of 103 MBLP mixed binary linear problem MCHH Markov-chain hyper-heuristic MdDE modified dither differential evolution MENOME metaheuristic pipe network optimisation model mIA modified immune algorithm MILP mixed integer linear programming MINLP mixed integer nonlinear programming MMAS max-min ant system MO multi-objective MODE multi-objective differential evolution MOEA multi-objective evolutionary algorithm MOGA multi-objective genetic algorithm MSATS mixed simulated annealing and tabu search NBGA non-crossover genetic algorithm with traditional bitwise mutation NFF needed fire flow NLP nonlinear programming NLP-DE combined nonlinear programming and differential evolution NSES non-dominated sorting evolution strategy NSGA-II non-dominated sorting genetic algorithm II OP operation OPTIMOGA optimised multi-objective genetic algorithm OPUS optimal power use surface PAR pitch adjustment rate (parameter) PESA-II Pareto envelope-based selection algorithm II PHSM prescreened heuristic sampling method PIV pipe index vector PRV pressure reducing valve PSF HS parameter setting free harmony search PSHS particle swarm harmony search PSO particle swarm optimisation PSO-DE combined particle swarm optimisation and differential evolution PVA present value analysis RC robust counterpart (approach) ROs real options (approach) RS random sampling RST random search technique SA simulated annealing SADE self-adaptive differential evolution SAMODE self-adaptive multi-objective differential evolution SCA shuffled complex algorithm SCE shuffled complex evolution SDE standard differential evolution SE search enforcement SFLA shuffled frog leaping algorithm SGA crossover-based genetic algorithm with bitwise mutation SMGA structured messy genetic algorithm SMODE standard multi-objective differential evolution (i.e., optimising the whole network directly without decomposition into subnetworks) SMORO scenario-based multi-objective robust optimisation SO single-objective SPEA2 strength Pareto evolutionary algorithm 2 Water 2018, 10, 307 29 of 103 SS scatter search SSSA scatter search using simulated annealing as a local searcher STA state transition algorithm TC time cycle TRS tank reserve size TS tabu search VSP variable speed pump WCEN water distribution cost-emission nexus WDS water distribution system WDSA water distribution systems analysis (conference) WPP water purification plant WSMGA water system multi-objective genetic algorithm WTP water treatment plant Appendix A Water 2018, 10, 307 30 of 103 Table A1....

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  • ...A great range of those methods has been applied to optimise design of WDSs to date, inclusive of (but not limited to) a GA [42,45,50,85,86,152–154], fmGA [88], non-crossover dither creeping mutation-based GA (CMBGA) [149], adaptive locally constrained GA (ALCO-GA) [155], SA [60], shuffled frog leaping algorithm (SFLA) [103], ACO [104,156], shuffled complex evolution (SCE) [157], harmony search (HS) [105,158,159], particle swarm HS (PSHS) [160], parameter setting free HS (PSF HS) [161], combined cuckoo-HS algorithm (CSHS) [162], particle swarm optimisation (PSO) [106,153,154], improved PSO (IPSO) [163], accelerated momentum PSO (AMPSO) [164], integer discrete PSO (IDPSO) [165], newly developed swarm-based optimisation (DSO) algorithm [150], scatter search (SS) [166], CE [61,62], immune algorithm (IA) [167], heuristic-based algorithm (HBA) [168], memetic algorithm (MA) [107], genetic heritage evolution by stochastic transmission (GHEST) [169], honey bee mating optimisation (HBMO) [170], DE [46,153,154,171], combined PSO and DE method (PSO-DE) [172], self-adaptive DE method (SADE) [173], NSGA-II [70], ES [68], NSES [92], cost gradient-based heuristic method [119],...

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Journal Article•DOI•
Energy and hydraulic efficiency in conventional water supply systems

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Mateus Ricardo Nogueira Vilanova1, José Antônio Perrella Balestieri1•
Sao Paulo State University1
01 Feb 2014-Renewable & Sustainable Energy Reviews
TL;DR: In this paper, state-of-the-art approaches to energy (electricity) and hydraulic efficiency and conservation in conventional water supply systems are presented, which can be classified into three dimensions: project and design dimension, operational dimension and physical dimension.
Abstract: This paper presents the state-of-the-art approaches to energy (electricity) and hydraulic efficiency and conservation in conventional water supply systems, providing an overview of energy efficiency and conservation alternatives from the analysis of selected research literature. These alternatives vary from leakage management to state-of-the art real-time optimization techniques, and can be classified into three dimensions according to their natures: project and design dimension, operational dimension and physical dimension. The potential energy savings and the impact of these alternatives over the water supply systems' energy efficiency are highly variable. All the energy efficiency and conservation alternatives analyzed in this work may contribute with the promotion of sustainability of conventional water supply systems.

86 citations

Journal Article•DOI•
Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

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M. Janga Reddy1, D. Nagesh Kumar2•
Indian Institute of Technology Bombay1, Indian Institute of Science2
01 Jan 2020
TL;DR: A review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems, and insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.
Abstract: During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

65 citations

Journal Article•DOI•
Optimal Design of Water Distribution Networks Using Parameter-Setting-Free Harmony Search for Two Major Parameters

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Zong Woo Geem1, Zong Woo Geem2, Yoon-Ho Cho2, Yoon-Ho Cho1•
Chung-Ang University1, Johns Hopkins University2
01 Jul 2011-Journal of Water Resources Planning and Management
TL;DR: This study proposes a novel parameter-setting-free technique for two major algorithm parameters (HMCR and PAR) and combines it with the harmony search algorithm and reaches the global optimum with good results.
Abstract: Although phenomenon-mimicking or metaheuristic algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, scatter search, hybrid particle swarm optimization, and honeybee mating optimization, have overcome the disadvantages of gradient-based algorithms when optimally designing water distribution networks, those algorithms require the extra tedious task of algorithm parameter setting. This study proposes a novel parameter-setting-free technique for two major algorithm parameters (HMCR and PAR) and combines it with the harmony search algorithm. When the proposed model is applied to the optimal design of a popular benchmark problem, it reaches the global optimum with good results. Thus, the technique is expected to be used in the real-world design process under a more user-friendly environment.

51 citations

Journal Article•DOI•
Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

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Wanqing Zhao1, Thomas Beach1, Yacine Rezgui1•
Cardiff University1
13 Apr 2016
TL;DR: A systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented.
Abstract: Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension, and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. First, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. This paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners.

50 citations

Cites methods from "Optimal Water Distribution Network ..."

  • ...Moreover, the scatter search (SS) (aiming at maintaining diverse and high quality solutions) [72], the immune algorithm (IA) (motivated by immunology in protecting the host organism from invaders) [73], and the honey-bee mating method (motivated by the biological behavior of honey bees) [74] were also applied to WDN optimization....

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References
More filters
Book•
Genetic algorithms in search, optimization, and machine learning

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David E. Goldberg
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

Journal Article•DOI•
Optimization by Simulated Annealing

[...]

Scott Kirkpatrick1, C. D. Gelatt1, Mario P. Vecchi2•
IBM1, Venezuelan Institute for Scientific Research2
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

41,772 citations

Genetic algorithms in search, optimization and machine learning

[...]

David E. Goldberg
01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations

"Optimal Water Distribution Network ..." refers methods in this paper

  • ...F or p er so na l u se o nl y; a ll ri gh ts r es er ve d. based on natural selection and genetics Goldberg 1989 had been successfully applied for the optimal WDN design Simpson et al. 1994; Savic and Walters 1997; Halhal et al. 1997; Gupta et al. 1999; Prasad and Park 2004; Kadu et al. 2008 ....

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Journal Article•DOI•
Ant system: optimization by a colony of cooperating agents

[...]

Marco Dorigo, Vittorio Maniezzo1, Alberto Colorni2•
University of Bologna1, Polytechnic University of Milan2
01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

11,224 citations

Journal Article•DOI•
Future paths for integer programming and links to artificial intelligence

[...]

Fred Glover1•
University of Colorado Boulder1
01 May 1986-Computers & Operations Research
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.

3,985 citations

"Optimal Water Distribution Network ..." refers methods in this paper

  • ...Tabu search that mimics the human memory process Glover 1986 was used by da Conceição Cunha and Rebeiro 2004 for optimal design of WDNs....

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Related Papers (5)
Design of optimal water distribution systems

[...]

01 Dec 1977-Water Resources Research
E. Alperovits, Uri Shamir
Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

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01 May 2003-Journal of Water Resources Planning and Management
Muzaffar Eusuff, Kevin Lansey
Water Distribution Network Design Optimization: Simulated Annealing Approach

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01 Jul 1999-Journal of Water Resources Planning and Management
Maria da Conceição Cunha, Joaquim Sousa
Genetic Algorithms for Least-Cost Design of Water Distribution Networks

[...]

01 Mar 1997-Journal of Water Resources Planning and Management
Dragan Savic, G. A. Walters
Ant Colony Optimization for Design of Water Distribution Systems

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15 Apr 2003-Journal of Water Resources Planning and Management
Holger R. Maier, Angus R. Simpson, Aaron C. Zecchin, Wai Kuan Foong, Kuang Yeow Phang, Hsin Yeow Seah, Chan Lim Tan