Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

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. Journal of Transportation Engineering, 112(3), 287-301. doi:10.1061/(asce)0733-947x(1986)112:3(287)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)Kleiner, 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)Dandy, 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)Izquierdo, J., Pérez, R., & Iglesias, P. L. (2004). Mathematical models and methods in the water industry. Mathematical and Computer Modelling, 39(11-12), 1353-1374. doi:10.1016/j.mcm.2004.06.012Giustolisi, 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)Montalvo, 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., 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-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.2010.01.015Martínez, J. B. (2010). Cost and reliability comparison between branched and looped water supply networks. Journal of Hydroinformatics, 12(2), 150-160. doi:10.2166/hydro.2009.080Goulter, I. C. (1992). Systems Analysis in Water‐Distribution Network Design: From Theory to Practice. Journal of Water Resources Planning and Management, 118(3), 238-248. doi:10.1061/(asce)0733-9496(1992)118:3(238)Park, 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)Khomsi, 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)Tanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Kalungi, 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)00168-6Morgan, D. R., & Goulter, I. C. (1985). Optimal urban water distribution design. Water Resources Research, 21(5), 642-652. doi:10.1029/wr021i005p00642Walters, G. A., & Knezevic, J. (1989). Discussion of « Reliability‐Based Optimization Model for Water Distribution Systems » by Yu‐Chun Su, Larry W. Mays, Ning Duan, and Kevin E. Lansey (December, 1987, Vol. 113, No. 12). Journal of Hydraulic Engineering, 115(8), 1157-1158. doi:10.1061/(asce)0733-9429(1989)115:8(1157)LOGANATHAN, G. V., SHERALI, H. D., & SHAH, M. P. (1990). A TWO-PHASE NETWORK DESIGN HEURISTIC FOR MINIMUM COST WATER DISTRIBUTION SYSTEMS UNDER A RELIABILITY CONSTRAINT. Engineering Optimization, 15(4), 311-336. doi:10.1080/03052159008941160Bouchart, F., & Goulter, I. (1991). Reliability Improvements in Design of Water Distribution Networks Recognizing Valve Location. Water Resources Research, 27(12), 3029-3040. doi:10.1029/91wr00590Gupta, 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)Xu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Su, Y., Mays, L. W., Duan, N., & Lansey, K. E. (1987). Reliability‐Based Optimization Model for Water Distribution Systems. Journal of Hydraulic Engineering, 113(12), 1539-1556. doi:10.1061/(asce)0733-9429(1987)113:12(1539)Cullinane, M. J., Lansey, K. E., & Mays, L. W. (1992). 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. IEEE Transactions on Neural Networks, 21(1), 11-25. doi:10.1109/tnn.2009.2033599Bo Shen, Zidong Wang, & Xiaohui Liu. (2011). Bounded $H_{\infty}$ Synchronization and State Estimation for Discrete Time-Varying Stochastic Complex Networks Over a Finite Horizon. IEEE Transactions on Neural Networks, 22(1), 145-157. doi:10.1109/tnn.2010.209066

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

Cooperative Particle Swarm Optimization for Combinatorial Problems

A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms. In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution. Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space

Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation

Abstract: Recently, losses reduction gained a great deal of attention in distribution system due to low-voltage level and the high-current passing through the lines, pushing the distribution utilities to improve their profit margins on one hand by reducing the unnecessary operational cost, and improving their delivered power quality on the other hand by maintaining the system reliability, and the continuity of supply for varying load demand. Load balancing, voltage regulation, network reconfiguration and others are different techniques used to reduce the losses. This study addresses the distribution network reconfiguration to minimise the network losses. A new modified form of particle swarm optimisation (PSO) is used to identify the optimal configuration of distribution network effectively. The difference between the modified PSO (MPSO) algorithms and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The suggested MPSO is tested via 33 and 69 IEEE networks. A benchmark comparison has been conducted to prove the effectiveness of MPSO compared with previous optimisation techniques

A new Risk-Managed planning of electric distribution network incorporating customer engagement and temporary solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach

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). Multiobjective Optimization of Concrete Frames by Simulated Annealing. Computer-Aided Civil and Infrastructure Engineering, 23(8), 596-610. doi:10.1111/j.1467-8667.2008.00561.xPinto, T., Praça, I., Vale, Z., Morais, H., & Sousa, T. M. (2013). Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis. Integrated Computer-Aided Engineering, 20(4), 335-346. doi:10.3233/ica-130438Putha, R., Quadrifoglio, L., & Zechman, E. (2011). Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions. Computer-Aided Civil and Infrastructure Engineering, 27(1), 14-28. doi:10.1111/j.1467-8667.2010.00715.xRaich, A. M., & Liszkai, T. R. (2011). Multi-objective Optimization of Sensor and Excitation Layouts for Frequency Response Function-Based Structural Damage Identification. Computer-Aided Civil and Infrastructure Engineering, 27(2), 95-117. doi:10.1111/j.1467-8667.2011.00726.xRodríguez-Seda, E. J., Stipanović, D. M., & Spong, M. W. (2012). Teleoperation of multi-agent systems with nonuniform control input delays. Integrated Computer-Aided Engineering, 19(2), 125-136. doi:10.3233/ica-2012-0396Saldarriaga , J. G. Bernal , A. Ochoa , S. 2008 Optimized design of water distribution network enlargements using resilience and dissipated power concepts 298 312Sarma, K. C., & Adeli, H. (2000). Fuzzy Genetic Algorithm for Optimization of Steel Structures. Journal of Structural Engineering, 126(5), 596-604. doi:10.1061/(asce)0733-9445(2000)126:5(596)Sgambi, L., Gkoumas, K., & Bontempi, F. (2012). Genetic Algorithms for the Dependability Assurance in the Design of a Long-Span Suspension Bridge. Computer-Aided Civil and Infrastructure Engineering, 27(9), 655-675. doi:10.1111/j.1467-8667.2012.00780.xShafahi, Y., & Bagherian, M. (2012). A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem. Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. doi:10.1111/j.1467-8667.2012.00769.xTanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Tao, H., Zain, J. M., Ahmed, M. M., Abdalla, A. N., & Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integrated Computer-Aided Engineering, 19(1), 81-91. doi:10.3233/ica-2012-0392Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Vamvakeridou-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)Vitins, B. J., & Axhausen, K. W. (2009). Optimization of Large Transport Networks Using the Ant Colony Heuristic. Computer-Aided Civil and Infrastructure Engineering, 24(1), 1-14. doi:10.1111/j.1467-8667.2008.00569.xVrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., & Sorooshian, S. (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research, 39(8). doi:10.1029/2002wr001746Vrugt, J. A., Ó Nualláin, B., Robinson, B. A., Bouten, W., Dekker, S. C., & Sloot, P. M. A. (2006). Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences, 32(8), 1139-1155. doi:10.1016/j.cageo.2005.10.015Vrugt , J. A. Robinson , B. A. 2007 Improved evolutionary search from genetically adaptive multi-search method 104 3 708 11Wu , Z. Y. Wang , R. H. Walski , T. M. Yang , S. Y. Bowdler , D. Baggett , C. C. 2006 Efficient pressure dependent demand model for large water distribution system analysisXie, C., & Waller, S. T. (2011). Optimal Routing with Multiple Objectives: Efficient Algorithm and Application to the Hazardous Materials Transportation Problem. Computer-Aided Civil and Infrastructure Engineering, 27(2), 77-94. doi:10.1111/j.1467-8667.2011.00720.xXu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Zeferino, J. A., Antunes, A. P., & Cunha, M. C. (2009). An Efficient Simulated Annealing Algorithm for Regional Wastewater System Planning. Computer-Aided Civil and Infrastructure Engineering, 24(5), 359-370. doi:10.1111/j.1467-8667.2009.00594.

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. 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). Multiobjective Optimization of Concrete Frames by Simulated Annealing. Computer-Aided Civil and Infrastructure Engineering, 23(8), 596-610. doi:10.1111/j.1467-8667.2008.00561.xPinto, T., Praça, I., Vale, Z., Morais, H., & Sousa, T. M. (2013). Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis. Integrated Computer-Aided Engineering, 20(4), 335-346. doi:10.3233/ica-130438Putha, R., Quadrifoglio, L., & Zechman, E. (2011). Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions. Computer-Aided Civil and Infrastructure Engineering, 27(1), 14-28. doi:10.1111/j.1467-8667.2010.00715.xRaich, A. M., & Liszkai, T. R. (2011). Multi-objective Optimization of Sensor and Excitation Layouts for Frequency Response Function-Based Structural Damage Identification. Computer-Aided Civil and Infrastructure Engineering, 27(2), 95-117. doi:10.1111/j.1467-8667.2011.00726.xRodríguez-Seda, E. J., Stipanović, D. M., & Spong, M. W. (2012). Teleoperation of multi-agent systems with nonuniform control input delays. Integrated Computer-Aided Engineering, 19(2), 125-136. doi:10.3233/ica-2012-0396Saldarriaga , J. G. Bernal , A. Ochoa , S. 2008 Optimized design of water distribution network enlargements using resilience and dissipated power concepts 298 312Sarma, K. C., & Adeli, H. (2000). Fuzzy Genetic Algorithm for Optimization of Steel Structures. Journal of Structural Engineering, 126(5), 596-604. doi:10.1061/(asce)0733-9445(2000)126:5(596)Sgambi, L., Gkoumas, K., & Bontempi, F. (2012). Genetic Algorithms for the Dependability Assurance in the Design of a Long-Span Suspension Bridge. Computer-Aided Civil and Infrastructure Engineering, 27(9), 655-675. doi:10.1111/j.1467-8667.2012.00780.xShafahi, Y., & Bagherian, M. (2012). A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem. Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. doi:10.1111/j.1467-8667.2012.00769.xTanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Tao, H., Zain, J. M., Ahmed, M. M., Abdalla, A. N., & Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integrated Computer-Aided Engineering, 19(1), 81-91. doi:10.3233/ica-2012-0392Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Vamvakeridou-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)Vitins, B. J., & Axhausen, K. W. (2009). Optimization of Large Transport Networks Using the Ant Colony Heuristic. Computer-Aided Civil and Infrastructure Engineering, 24(1), 1-14. doi:10.1111/j.1467-8667.2008.00569.xVrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., & Sorooshian, S. (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research, 39(8). doi:10.1029/2002wr001746Vrugt, J. A., Ó Nualláin, B., Robinson, B. A., Bouten, W., Dekker, S. C., & Sloot, P. M. A. (2006). Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences, 32(8), 1139-1155. doi:10.1016/j.cageo.2005.10.015Vrugt , J. A. Robinson , B. A. 2007 Improved evolutionary search from genetically adaptive multi-search method 104 3 708 11Wu , Z. Y. Wang , R. H. Walski , T. M. Yang , S. Y. Bowdler , D. Baggett , C. C. 2006 Efficient pressure dependent demand model for large water distribution system analysisXie, C., & Waller, S. T. (2011). Optimal Routing with Multiple Objectives: Efficient Algorithm and Application to the Hazardous Materials Transportation Problem. Computer-Aided Civil and Infrastructure Engineering, 27(2), 77-94. doi:10.1111/j.1467-8667.2011.00720.xXu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Zeferino, J. A., Antunes, A. P., & Cunha, M. C. (2009). An Efficient Simulated Annealing Algorithm for Regional Wastewater System Planning. Computer-Aided Civil and Infrastructure Engineering, 24(5), 359-370. doi:10.1111/j.1467-8667.2009.00594.