Ant colony optimization for design of water distribution systems

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms ~ACOAs!, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms ~GAs!. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.Holger R. Maier, Angus R. Simpson, Aaron C. Zecchin, Wai Kuan Foong, Kuang Yeow Phang, Hsin Yeow Seah and Chan Lim Ta

DSS Architecture for Water Uses Management

AbstractManagement of water uses requests to harmonize demands and needs which are getting more and more complex and sophisticated. During the past 3 decades, modelling systems for hydrology, hydraulics and water quality have been used as stand alone products and were used in order to produce an analysis of a current situation and to generate forecast according to different horizons. The current situation, characterized by the fast increase of monitoring devices mainly in the urban environments, requests an integration of the modelling tools in global information systems that are now dedicated to the global management of urban environments and related services. Energy distribution, water distribution, solid wastes collection, traffic optimization are today major issues for cities that are looking for functional Decisions Supports Systems that may integrated the various components and operate in a sustainable perspective. The modelling systems used for hydrology, hydraulic and water quality have to integrate a common framework allowing modular approach and interoperability. The paper presents a generic operational approach that could be implemented in order to address the management of water uses in a complex urban environment: water supply security issues from groundwater resources, inundation risk and water resources management under the perspective of climate change. The architecture is based on the interoperability of the various models and is integrated in a platform allowing to organize the workflows of data and the production of real time information's used by the decision makers. The current approached is implemented within the AquaVar project on the Var catchment located in the French Riviera

On the Complexities of the Design of Water Distribution Networks

Water supply is one of the most recognizable and important public services contributing to quality of life. Water distribution networks WDNs are extremely complex assets. A number of complex tasks, such as design, planning, operation, maintenance, and management, are inherently associated with such networks. In this paper, we focus on the design of a WDN, which is a wide and open problem in hydraulic engineering. This problem is a large-scale combinatorial, nonlinear, nonconvex, multiobjective optimization problem, involving various types of decision variables and many complex implicit constraints. To handle this problem, we provide a synergetic association between swarm intelligence and multiagent systems where human interaction is also enabled. This results in a powerful collaborative system for finding solutions to such a complex hydraulic engineering problem. All the ingredients have been integrated into a software tool that has also been shown to efficiently solve problems from other engineering fields.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Educacion y Ciencia, and ACOMP/2010/146 of the Conselleria d'Educacio of the Generalitat Valenciana. The first author is also indebted to the Universitat Politecnica de Valencia for the sabbatical leave granted during the first semester of 2011. The use of English in this paper was revised by John Rawlins.Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R.; Matías, A. (2012). On the Complexities of the Design of Water Distribution Networks. Mathematical Problems in Engineering. 2012:1-25. https://doi.org/10.1155/2012/9479611252012Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. 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Optimization‐Availability‐Based Design of Water‐Distribution Networks. Journal of Hydraulic Engineering, 118(3), 420-441. doi:10.1061/(asce)0733-9429(1992)118:3(420)Vamvakeridou-Lyroudia, L. S., Walters, G. A., & Savic, D. A. (2005). Fuzzy Multiobjective Optimization of Water Distribution Networks. Journal of Water Resources Planning and Management, 131(6), 467-476. doi:10.1061/(asce)0733-9496(2005)131:6(467)Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Dong, Y., Tang, J., Xu, B., & Wang, D. (2005). An application of swarm optimization to nonlinear programming. Computers & Mathematics with Applications, 49(11-12), 1655-1668. doi:10.1016/j.camwa.2005.02.006Jin, Y.-X., Cheng, H.-Z., Yan, J., & Zhang, L. (2007). New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research, 77(3-4), 227-233. doi:10.1016/j.epsr.2006.02.016Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Molina, J., Santana, L. V., Hernández-Díaz, A. G., Coello Coello, C. A., & Caballero, R. (2009). g-dominance: Reference point based dominance for multiobjective metaheuristics. European Journal of Operational Research, 197(2), 685-692. doi:10.1016/j.ejor.2008.07.01510.1029/89WR02879. (2010). Water Resources Research. doi:10.1029/89wr02879Savic, D. A., & Walters, G. A. (1997). Genetic Algorithms for Least-Cost Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 123(2), 67-77. doi:10.1061/(asce)0733-9496(1997)123:2(67)Zecchin, A. C., Simpson, A. R., Maier, H. R., & Nixon, J. B. (2005). Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization. IEEE Transactions on Evolutionary Computation, 9(2), 175-191. doi:10.1109/tevc.2005.844168Yurong Liu, Zidong Wang, Jinling Liang, & Xiaohui Liu. (2009). Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays. IEEE Transactions on Neural Networks, 20(7), 1102-1116. doi:10.1109/tnn.2009.2016210Jinling Liang, Zidong Wang, & Xiaohui Liu. (2009). State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case. IEEE Transactions on Neural Networks, 20(5), 781-793. doi:10.1109/tnn.2009.2013240Zidong Wang, Yao Wang, & Yurong Liu. (2010). Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays. 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A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. (2015). A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching. Engineering Optimization. 1-13. doi:10.1080/0305215X.2015.1107434S113Becker, U., & Fahrmeir, L. (2001). Bump Hunting for Risk: a New Data Mining Tool and its Applications. Computational Statistics, 16(3), 373-386. doi:10.1007/s001800100073Bouguessa, M., & Shengrui Wang. (2009). Mining Projected Clusters in High-Dimensional Spaces. IEEE Transactions on Knowledge and Data Engineering, 21(4), 507-522. doi:10.1109/tkde.2008.162Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1-2), 103-112. doi:10.1016/j.chemolab.2004.12.011CHONG, I., & JUN, C. (2008). Flexible patient rule induction method for optimizing process variables in discrete type. Expert Systems with Applications, 34(4), 3014-3020. doi:10.1016/j.eswa.2007.05.047Cole, S. W., Galic, Z., & Zack, J. A. (2003). Controlling false-negative errors in microarray differential expression analysis: a PRIM approach. Bioinformatics, 19(14), 1808-1816. doi:10.1093/bioinformatics/btg242FRIEDMAN, J. H., & FISHER, N. I. (1999). Statistics and Computing, 9(2), 123-143. doi:10.1023/a:1008894516817Geem, Z. W. (2006). Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38(3), 259-277. doi:10.1080/03052150500467430Goncalves, L. B., Vellasco, M. M. B. R., Pacheco, M. A. C., & Flavio Joaquim de Souza. (2006). Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 36(2), 236-248. doi:10.1109/tsmcc.2004.843220Hastie, T., Friedman, J., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer Series in Statistics. doi:10.1007/978-0-387-21606-5Chih-Ming Hsu, & Ming-Syan Chen. (2009). On the Design and Applicability of Distance Functions in High-Dimensional Data Space. IEEE Transactions on Knowledge and Data Engineering, 21(4), 523-536. doi:10.1109/tkde.2008.178Hwang, S.-F., & He, R.-S. (2006). A hybrid real-parameter genetic algorithm for function optimization. Advanced Engineering Informatics, 20(1), 7-21. doi:10.1016/j.aei.2005.09.001Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Javadi, A. A., Farmani, R., & Tan, T. P. (2005). A hybrid intelligent genetic algorithm. Advanced Engineering Informatics, 19(4), 255-262. doi:10.1016/j.aei.2005.07.003Jin, X., Zhang, J., Gao, J., & Wu, W. (2008). Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II. Journal of Zhejiang University-SCIENCE A, 9(3), 391-400. doi:10.1631/jzus.a071448Johns, M. B., Keedwell, E., & Savic, D. (2014). Adaptive locally constrained genetic algorithm for least-cost water distribution network design. Journal of Hydroinformatics, 16(2), 288-301. doi:10.2166/hydro.2013.218Jourdan, L., Corne, D., Savic, D., & Walters, G. (2005). Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design. Evolutionary Multi-Criterion Optimization, 841-855. doi:10.1007/978-3-540-31880-4_58Kamwa, I., Samantaray, S. R., & Joos, G. (2009). Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area Post-Disturbance Records. IEEE Transactions on Power Systems, 24(1), 258-270. doi:10.1109/tpwrs.2008.2009430Kang, D., & Lansey, K. (2012). Revisiting Optimal Water-Distribution System Design: Issues and a Heuristic Hierarchical Approach. Journal of Water Resources Planning and Management, 138(3), 208-217. doi:10.1061/(asce)wr.1943-5452.0000165Keedwell, E., & Khu, S.-T. (2005). A hybrid genetic algorithm for the design of water distribution networks. Engineering Applications of Artificial Intelligence, 18(4), 461-472. doi:10.1016/j.engappai.2004.10.001Kehl, V., & Ulm, K. (2006). Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis, 50(5), 1338-1355. doi:10.1016/j.csda.2004.11.015Liu, X., Minin, V., Huang, Y., Seligson, D. B., & Horvath, S. (2004). Statistical Methods for Analyzing Tissue Microarray Data. Journal of Biopharmaceutical Statistics, 14(3), 671-685. doi:10.1081/bip-200025657Marchi, A., Dandy, G., Wilkins, A., & Rohrlach, H. (2014). Methodology for Comparing Evolutionary Algorithms for Optimization of Water Distribution Systems. Journal of Water Resources Planning and Management, 140(1), 22-31. doi:10.1061/(asce)wr.1943-5452.0000321Martínez-Rodríguez, J. B., Montalvo, I., Izquierdo, J., & Pérez-García, R. (2011). Reliability and Tolerance Comparison in Water Supply Networks. Water Resources Management, 25(5), 1437-1448. doi:10.1007/s11269-010-9753-2McClymont, K., Keedwell, E., Savić, D., & Randall-Smith, M. (2013). A general multi-objective hyper-heuristic for water distribution network design with discolouration risk. Journal of Hydroinformatics, 15(3), 700-716. doi:10.2166/hydro.2012.022McClymont, K., Keedwell, E. C., Savić, D., & Randall-Smith, M. (2014). Automated construction of evolutionary algorithm operators for the bi-objective water distribution network design problem using a genetic programming based hyper-heuristic approach. Journal of Hydroinformatics, 16(2), 302-318. doi:10.2166/hydro.2013.226Michalski, R. S. (2000). Machine Learning, 38(1/2), 9-40. doi:10.1023/a:1007677805582Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.12062Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Nguyen, V. V., Hartmann, D., & König, M. (2012). A distributed agent-based approach for simulation-based optimization. Advanced Engineering Informatics, 26(4), 814-832. doi:10.1016/j.aei.2012.06.001Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., … Chan-Hilton, A. (2010). State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management. 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An Integrated Modeling Approach to Optimize the Management of a Water Distribution System: Improving the Sustainability While Dealing with Water Loss, Energy Consumption and Environmental Impacts

Research article There is a strong link between water and energy in municipal water systems then the Alliance to Save Energy coined the term "Watergy" [1]. Each component of the integrated water system contributes differently to the energy balance. With regard to urban water distribution systems (WDS), the pumping energy cost represents the single largest part of the total operational cost, also magnified by every litre of water lost to leaks. Even a small increase in operational efficiency may result in significant cost savings to the water industries. Therefore the inefficient management of water distribution systems results not only into depletion of water resources but also into energy consumption that increase CO2 emissions related also to the treatment of water volumes greater than needed, with use of excessive chemical components and consequent higher environmental global impact. The research outlined in this contribution was born with the aim to develop appropriate methodologies and tools to support the optimization of the WDS performance, in terms of water saving and reduction of energy consumptions and consequently environmental impacts. The integration of advanced WDS hydraulic modelling with a material and energy flow analysis is proposed herein, where the output of the hydraulic simulations permits to get more accurate input for a metabolic analysis of the system Next phases of this research will test the integrated model under different scenarios, aimed at quantifying the environmental impact of different WDS management solutions by means of selected indicator

Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Spanish Ministry of Education and Science, and ACOMP/2010/146 of the education department of the Generalitat Valenciana. The use of English was revised by John Rawlins.Montalvo Arango, I.; Izquierdo Sebastián, J.; Pérez García, R.; Herrera Fernández, AM. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering. 29(6):433-448. https://doi.org/10.1111/mice.12062S433448296Adeli, H., & Kumar, S. (1995). Distributed Genetic Algorithm for Structural Optimization. Journal of Aerospace Engineering, 8(3), 156-163. doi:10.1061/(asce)0893-1321(1995)8:3(156)Afshar, M. H., Akbari, M., & Mariño, M. A. (2005). Simultaneous Layout and Size Optimization of Water Distribution Networks: Engineering Approach. Journal of Infrastructure Systems, 11(4), 221-230. doi:10.1061/(asce)1076-0342(2005)11:4(221)Amini, F., Hazaveh, N. K., & Rad, A. A. (2013). Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers. Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-557. doi:10.1111/mice.12017Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Badawy, R., Yassine, A., Heßler, A., Hirsch, B., & Albayrak, S. (2013). A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid. Integrated Computer-Aided Engineering, 20(2), 127-141. doi:10.3233/ica-130423Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. doi:10.1007/bf00940812Dandy, G. C., & Engelhardt, M. O. (2006). Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains. Journal of Water Resources Planning and Management, 132(2), 79-88. doi:10.1061/(asce)0733-9496(2006)132:2(79)Díaz , J. L. Herrera , M. Izquierdo , J. Montalvo , I. Pérez-García , R. 2008 A particle swarm optimization derivative applied to cluster analysisDorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436Dridi, L., Parizeau, M., Mailhot, A., & Villeneuve, J.-P. (2008). Using Evolutionary Optimization Techniques for Scheduling Water Pipe Renewal Considering a Short Planning Horizon. Computer-Aided Civil and Infrastructure Engineering, 23(8), 625-635. doi:10.1111/j.1467-8667.2008.00564.xDuan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3), 501-521. doi:10.1007/bf00939380Duchesne, S., Beardsell, G., Villeneuve, J.-P., Toumbou, B., & Bouchard, K. (2012). A Survival Analysis Model for Sewer Pipe Structural Deterioration. Computer-Aided Civil and Infrastructure Engineering, 28(2), 146-160. doi:10.1111/j.1467-8667.2012.00773.xDupont, G., Adam, S., Lecourtier, Y., & Grilheres, B. (2008). Multi objective particle swarm optimization using enhanced dominance and guide selection. International Journal of Computational Intelligence Research, 4(2). doi:10.5019/j.ijcir.2008.134Fougères, A.-J., & Ostrosi, E. (2013). Fuzzy agent-based approach for consensual design synthesis in product configuration. Integrated Computer-Aided Engineering, 20(3), 259-274. doi:10.3233/ica-130434Fuggini, C., Chatzi, E., & Zangani, D. (2012). Combining Genetic Algorithms with a Meso-Scale Approach for System Identification of a Smart Polymeric Textile. Computer-Aided Civil and Infrastructure Engineering, 28(3), 227-245. doi:10.1111/j.1467-8667.2012.00789.xZong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)Goulter, I. C., & Bouchart, F. (1990). Reliability‐Constrained Pipe Network Model. Journal of Hydraulic Engineering, 116(2), 211-229. doi:10.1061/(asce)0733-9429(1990)116:2(211)Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. Journal of Transportation Engineering, 112(3), 287-301. doi:10.1061/(asce)0733-947x(1986)112:3(287)Gupta, R., & Bhave, P. R. (1994). Reliability Analysis of Water‐Distribution Systems. Journal of Environmental Engineering, 120(2), 447-461. doi:10.1061/(asce)0733-9372(1994)120:2(447)Gutierrez-Garcia, J. O., & Sim, K. M. (2012). Agent-based cloud workflow execution. Integrated Computer-Aided Engineering, 19(1), 39-56. doi:10.3233/ica-2012-0387Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P., & Yu, W.-D. (2012). Neuro-Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction. Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-781. doi:10.1111/j.1467-8667.2012.00786.xIzquierdo , J. Minciardi , R. Montalvo , I. Robba , M. Tavera , M. 2008a Particle swarm optimization for the biomass supply chain strategic planning 1272 80Izquierdo , J. Montalvo , I. Herrera , M. Pérez-García , R. 2012 A general purpose non-linear optimization framework based on particle swarm optimizationIzquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Izquierdo , J. Montalvo , I. Pérez , R. Tavera , M. 2008b Optimization in water systems: a PSO approach 239 46Jafarkhani, R., & Masri, S. F. (2010). Finite Element Model Updating Using Evolutionary Strategy for Damage Detection. Computer-Aided Civil and Infrastructure Engineering, 26(3), 207-224. doi:10.1111/j.1467-8667.2010.00687.xJanson, S., Merkle, D., & Middendorf, M. (2008). Molecular docking with multi-objective Particle Swarm Optimization. Applied Soft Computing, 8(1), 666-675. doi:10.1016/j.asoc.2007.05.005Kalungi, P., & Tanyimboh, T. T. (2003). Redundancy model for water distribution systems. Reliability Engineering & System Safety, 82(3), 275-286. doi:10.1016/s0951-8320(03)00168-6Keedwell, E., & Khu, S.-T. (2006). Novel Cellular Automata Approach to Optimal Water Distribution Network Design. Journal of Computing in Civil Engineering, 20(1), 49-56. doi:10.1061/(asce)0887-3801(2006)20:1(49)Kennedy , J. Eberhart , R. C. 1995 Particle swarm optimization 1942 48Khomsi, D., Walters, G. A., Thorley, A. R. D., & Ouazar, D. (1996). Reliability Tester for Water-Distribution Networks. Journal of Computing in Civil Engineering, 10(1), 10-19. doi:10.1061/(asce)0887-3801(1996)10:1(10)KIM, H., & ADELI, H. (2001). DISCRETE COST OPTIMIZATION OF COMPOSITE FLOORS USING A FLOATING-POINT GENETIC ALGORITHM. Engineering Optimization, 33(4), 485-501. doi:10.1080/03052150108940930Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Kleiner, Y., Adams, B. J., & Rogers, J. S. (2001). Water Distribution Network Renewal Planning. Journal of Computing in Civil Engineering, 15(1), 15-26. doi:10.1061/(asce)0887-3801(2001)15:1(15)Martínez-Rodríguez, J. B., Montalvo, I., Izquierdo, J., & Pérez-García, R. (2011). Reliability and Tolerance Comparison in Water Supply Networks. Water Resources Management, 25(5), 1437-1448. doi:10.1007/s11269-010-9753-2Montalvo Arango, I. (s. f.). Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization. doi:10.4995/thesis/10251/14858Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Montalvo, I., Izquierdo, J., Pérez, R., & Iglesias, P. L. (2008). A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Engineering Optimization, 40(7), 655-668. doi:10.1080/03052150802010607Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization applied to the design of water supply systems. Computers & Mathematics with Applications, 56(3), 769-776. doi:10.1016/j.camwa.2008.02.006Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Moscato , P. 1989 On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic AlgorithmsNejat, A., & Damnjanovic, I. (2012). Agent-Based Modeling of Behavioral Housing Recovery Following Disasters. Computer-Aided Civil and Infrastructure Engineering, 27(10), 748-763. doi:10.1111/j.1467-8667.2012.00787.xPark, H., & Liebman, J. C. (1993). Redundancy‐Constrained Minimum‐Cost Design of Water‐Distribution Nets. Journal of Water Resources Planning and Management, 119(1), 83-98. doi:10.1061/(asce)0733-9496(1993)119:1(83)Paya, I., Yepes, V., González-Vidosa, F., & Hospitaler, A. (2008). 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Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Spanish Ministry of Education and Science, and ACOMP/2010/146 of the education department of the Generalitat Valenciana. The use of English was revised by John Rawlins.Montalvo Arango, I.; Izquierdo Sebastián, J.; Pérez García, R.; Herrera Fernández, AM. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering. 29(6):433-448. https://doi.org/10.1111/mice.12062433448296Adeli, H., & Kumar, S. (1995). Distributed Genetic Algorithm for Structural Optimization. Journal of Aerospace Engineering, 8(3), 156-163. doi:10.1061/(asce)0893-1321(1995)8:3(156)Afshar, M. H., Akbari, M., & Mariño, M. A. (2005). Simultaneous Layout and Size Optimization of Water Distribution Networks: Engineering Approach. Journal of Infrastructure Systems, 11(4), 221-230. doi:10.1061/(asce)1076-0342(2005)11:4(221)Amini, F., Hazaveh, N. K., & Rad, A. A. (2013). Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers. Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-557. doi:10.1111/mice.12017Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Badawy, R., Yassine, A., Heßler, A., Hirsch, B., & Albayrak, S. (2013). A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid. Integrated Computer-Aided Engineering, 20(2), 127-141. doi:10.3233/ica-130423Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. doi:10.1007/bf00940812Dandy, G. C., & Engelhardt, M. O. (2006). Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains. Journal of Water Resources Planning and Management, 132(2), 79-88. doi:10.1061/(asce)0733-9496(2006)132:2(79)Díaz , J. L. Herrera , M. Izquierdo , J. Montalvo , I. Pérez-García , R. 2008 A particle swarm optimization derivative applied to cluster analysisDorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436Dridi, L., Parizeau, M., Mailhot, A., & Villeneuve, J.-P. (2008). Using Evolutionary Optimization Techniques for Scheduling Water Pipe Renewal Considering a Short Planning Horizon. Computer-Aided Civil and Infrastructure Engineering, 23(8), 625-635. doi:10.1111/j.1467-8667.2008.00564.xDuan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3), 501-521. doi:10.1007/bf00939380Duchesne, S., Beardsell, G., Villeneuve, J.-P., Toumbou, B., & Bouchard, K. (2012). A Survival Analysis Model for Sewer Pipe Structural Deterioration. Computer-Aided Civil and Infrastructure Engineering, 28(2), 146-160. doi:10.1111/j.1467-8667.2012.00773.xDupont, G., Adam, S., Lecourtier, Y., & Grilheres, B. (2008). Multi objective particle swarm optimization using enhanced dominance and guide selection. International Journal of Computational Intelligence Research, 4(2). doi:10.5019/j.ijcir.2008.134Fougères, A.-J., & Ostrosi, E. (2013). Fuzzy agent-based approach for consensual design synthesis in product configuration. Integrated Computer-Aided Engineering, 20(3), 259-274. doi:10.3233/ica-130434Fuggini, C., Chatzi, E., & Zangani, D. (2012). Combining Genetic Algorithms with a Meso-Scale Approach for System Identification of a Smart Polymeric Textile. Computer-Aided Civil and Infrastructure Engineering, 28(3), 227-245. doi:10.1111/j.1467-8667.2012.00789.xZong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)Goulter, I. C., & Bouchart, F. (1990). Reliability‐Constrained Pipe Network Model. Journal of Hydraulic Engineering, 116(2), 211-229. doi:10.1061/(asce)0733-9429(1990)116:2(211)Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. Journal of Transportation Engineering, 112(3), 287-301. doi:10.1061/(asce)0733-947x(1986)112:3(287)Gupta, R., & Bhave, P. R. (1994). Reliability Analysis of Water‐Distribution Systems. Journal of Environmental Engineering, 120(2), 447-461. doi:10.1061/(asce)0733-9372(1994)120:2(447)Gutierrez-Garcia, J. O., & Sim, K. M. (2012). Agent-based cloud workflow execution. Integrated Computer-Aided Engineering, 19(1), 39-56. doi:10.3233/ica-2012-0387Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P., & Yu, W.-D. (2012). Neuro-Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction. Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-781. doi:10.1111/j.1467-8667.2012.00786.xIzquierdo , J. Minciardi , R. Montalvo , I. Robba , M. Tavera , M. 2008a Particle swarm optimization for the biomass supply chain strategic planning 1272 80Izquierdo , J. Montalvo , I. Herrera , M. Pérez-García , R. 2012 A general purpose non-linear optimization framework based on particle swarm optimizationIzquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Izquierdo , J. Montalvo , I. Pérez , R. Tavera , M. 2008b Optimization in water systems: a PSO approach 239 46Jafarkhani, R., & Masri, S. F. (2010). Finite Element Model Updating Using Evolutionary Strategy for Damage Detection. Computer-Aided Civil and Infrastructure Engineering, 26(3), 207-224. doi:10.1111/j.1467-8667.2010.00687.xJanson, S., Merkle, D., & Middendorf, M. (2008). Molecular docking with multi-objective Particle Swarm Optimization. Applied Soft Computing, 8(1), 666-675. doi:10.1016/j.asoc.2007.05.005Kalungi, P., & Tanyimboh, T. T. (2003). Redundancy model for water distribution systems. Reliability Engineering & System Safety, 82(3), 275-286. doi:10.1016/s0951-8320(03)00168-6Keedwell, E., & Khu, S.-T. (2006). Novel Cellular Automata Approach to Optimal Water Distribution Network Design. Journal of Computing in Civil Engineering, 20(1), 49-56. doi:10.1061/(asce)0887-3801(2006)20:1(49)Kennedy , J. Eberhart , R. C. 1995 Particle swarm optimization 1942 48Khomsi, D., Walters, G. A., Thorley, A. R. D., & Ouazar, D. (1996). Reliability Tester for Water-Distribution Networks. Journal of Computing in Civil Engineering, 10(1), 10-19. doi:10.1061/(asce)0887-3801(1996)10:1(10)KIM, H., & ADELI, H. (2001). DISCRETE COST OPTIMIZATION OF COMPOSITE FLOORS USING A FLOATING-POINT GENETIC ALGORITHM. Engineering Optimization, 33(4), 485-501. doi:10.1080/03052150108940930Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Kleiner, Y., Adams, B. J., & Rogers, J. S. (2001). Water Distribution Network Renewal Planning. Journal of Computing in Civil Engineering, 15(1), 15-26. doi:10.1061/(asce)0887-3801(2001)15:1(15)Martínez-Rodríguez, J. B., Montalvo, I., Izquierdo, J., & Pérez-García, R. (2011). Reliability and Tolerance Comparison in Water Supply Networks. Water Resources Management, 25(5), 1437-1448. doi:10.1007/s11269-010-9753-2Montalvo Arango, I. (s. f.). Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization. doi:10.4995/thesis/10251/14858Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Montalvo, I., Izquierdo, J., Pérez, R., & Iglesias, P. L. (2008). A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Engineering Optimization, 40(7), 655-668. doi:10.1080/03052150802010607Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization applied to the design of water supply systems. 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Improving the performance of water distribution systems’ simulation on multicore systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-015-1607-5Hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDS) are used extensively, and their computational performance is key when considering optimization problems. This paper presents an approach to speedup the hydraulic solver using OpenMP with two efficient methods for WDS simulation. The paper identifies the different tasks carried out in the simulation, showing their contribution to the execution time, and selecting the target tasks for parallelization. After describing the algorithms for the selected tasks, parallel OpenMP versions are derived, with emphasis on the task of linear system update. Results are presented for four different large WDS models, showing considerable reduction in computing timeThis work has been partially supported by Ministerio de Economia y Competitividad from Spain, under the project TEC2012-38142-C04-01, and by project PROMETEO FASE II 2014/003 of Generalitat Valenciana.Alvarruiz Bermejo, F.; Martínez Alzamora, F.; Vidal Maciá, AM. (2016). Improving the performance of water distribution systems’ simulation on multicore systems. Journal of Supercomputing. 1-13. https://doi.org/10.1007/s11227-015-1607-5S113Abraham E, Stoianov I (2015) Efficient preconditioned iterative methods for hydraulic simulation of large scale water distribution networks. Proc Eng 119:623–632Abraham E, Stoianov I (2015) Sparse null space algorithms for hydraulic analysis of large-scale water supply networks. J Hydraul Eng. doi: 10.1061/(ASCE)HY.1943-7900.0001089Alonso JM, Alvarruiz F, Guerrero D et al (2000) Parallel computing in water network analysis and leakage minimization. J Water Resour Plan Manag 126(4):251–260Alvarruiz F, Martínez-Alzamora F, Vidal AM (2015) Efficient simulation of water distribution systems using openmp. In: 15th International conference computational and mathematical methods in computational mathematics, science and engineering, pp 125–129Alvarruiz F, Martínez-Alzamora F, Vidal AM (2015) Improving the efficiency of the loop method for the simulation of water distribution systems. J Water Resour Plan Manag 141(10):04015019Burger G, Sitzenfrei R, Kleidorfer M, Rauch W (2015) Quest for a new solver for EPANET 2. J Water Resour Plan Manag. doi: 10.1061/(ASCE)WR.1943-5452.0000596Creaco E, Franchini M (2014) Comparison of Newton–Raphson global and loop algorithms for water distribution network resolution. J Hydraul Eng 140(3):313–321Creaco E, Franchini M (2015) The identification of loops in water distribution networks. Proc Eng 119:506–515 Computing and Control for the Water Industry (CCWI2015) Sharing the best practice in water managementCrous PA, van Zyl JE, Roodt Y (2012) The potential of graphical processing units to solve hydraulic network equations. J Hydroinf 14:603–612Elhay S, Simpson A, Deuerlein J, Alexander B, Schilders W (2014) Reformulated co-tree flows method competitive with the global gradient algorithm for solving water distribution system equations. J Water Resour Plan Manag 140(12):04014040Epp R, Fowler AG (1970) Efficient code for steady-state flows in networks. J Hydraul Div 96(1):43–56Guidolin M, Burovskiy P, Kapelan Z, Savić D (2010) Cwsnet: an object-oriented toolkit for water distribution system simulations. In: Proceedings of 12th water distribution system analysis symposium, ASCE, Reston, VAGuidolin M, Kapelan Z, Savic D (2013) Using high performance techniques to accelerate demand-driven hydraulic solvers. J Hydroinf 15(1):38–54Guidolin M, Kapelan Z, Savic D, Giustolisi O (2010) High performance hydraulic simulations with epanet on graphics processing units. In: Proceedings of 9th international conference on hydroinformaticsOstfeld A, Uber J, Salomons E et al (2008) The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J Water Resour Plan Manag 134(6):556–568Rossman AL (2000) Epanet 2 users manual. Water Supply and Water Resources Division, US Environment Protection AgencyTodini E, Pilati S (1988) Computer applications in water supply: vol. 1—systems analysis and simulation. In: Coulbeck B, Orr CH (eds) A gradient algorithm for the analysis of pipe networks. Research Studies Press Ltd, Letchworth, Hertfordshire, UK, pp 1–2

Incorporation of variable-speed pumping in multiobjective genetic algorithm optimization of the design of water transmission systems

Global warming caused by human activities presents serious global risks. Individuals, governments, and industries need to be more energy efficient and contribute to the mitigation of global warming by reducing their greenhouse gas (GHG) emissions. In previous research, GHG emission reduction has been identified as one important criterion in improving the sustainability of urban infrastructure and urban water systems.Within the water industry, opportunities exist for reducing GHG emissions by improving pumping efficiency via the use of variable-speed pumps (VSPs). Previously, VSPs have been used in the optimization of the operation of existing water distribution systems (WDSs). However, in WDS design optimization problems, fixed-speed pumps (FSPs) are commonly used. In this study, a pump power estimation method, developed using a false position method based optimization approach, is proposed to incorporate VSPs in the conceptual design or planning of water transmission systems (WTSs), using optimization. This pump power estimation method is implemented within the solution evaluation process via a multiobjective genetic algorithm approach. A case study is used to demonstrate the application of the pump power estimation method in estimating pump power and associated energy consumption of VSPs and FSPs in WTS optimization. In addition, comparisons are made between variable-speed pumping and fixed-speed pumping in multiobjective WTS optimization accounting for total cost and GHG emissions. The results show that the use of variable-speed pumping leads to significant savings in both total cost and GHG emissions from WTSs for the case study considere. © 2012 American Society of Civil Engineers.Wenyan Wu, Angus R. Simpson, Holger R. Maier and Angela March