The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks

This paper presents a modified Shuffled Frog Leaping Algorithm (SFLA) applied to the design of water distribution networks. Generally, one of the major disadvantages of the traditional SFLA is the high number of parameters that need to be calibrated for proper operation of the algorithm. A method for calibrating these parameters is presented and applied to the design of three benchmark medium-sized networks widely known in the literature (Hanoi, New York Tunnel, and GoYang). For each of the problems, over 35,000 simulations were conducted. Then, a statistical analysis was performed, and the relative importance of each of the parameters was analyzed to achieve the best possible configuration of the modified SFLA. The main conclusion from this study is that not all of the original SFL algorithm parameters are important. Thus, the fraction of frogs in the memeplex q can be eliminated, while the other parameters (number of evolutionary steps Ns, number of memeplexes m, and number of frogs n) may be set to constant values that run optimally for all medium-sized networks. Furthermore, the modified acceleration parameter C becomes the key parameter in the calibration process, vastly improving the results provided by the original SFLA.This work was supported by the Program Initiation into research (Project 11140128) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile. This work was also supported by the project DPI2009-13674 (OPERAGUA) of the Direccion General de Investigacion y Gestion del Plan Nacional de I + D + I del Ministerio de Ciencia e Innovacion, Spain.Mora Meliá, D.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Muñoz-Velasco, P. (2016). The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks. Water. 2016(8). https://doi.org/10.3390/w8050182S20168Alperovits, E., & Shamir, U. (1977). Design of optimal water distribution systems. Water Resources Research, 13(6), 885-900. doi:10.1029/wr013i006p00885Fujiwara, O., & Khang, D. B. (1990). A two-phase decomposition method for optimal design of looped water distribution networks. Water Resources Research, 26(4), 539-549. doi:10.1029/wr026i004p00539Su, 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)Chung, G., & Lansey, K. (2008). Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System. Water Resources Management, 23(4), 797-823. doi:10.1007/s11269-008-9300-6Lansey, K. E., & Mays, L. W. (1989). Optimization Model for Water Distribution System Design. Journal of Hydraulic Engineering, 115(10), 1401-1418. doi:10.1061/(asce)0733-9429(1989)115:10(1401)Martínez-Solano, J., Iglesias-Rey, P. L., Pérez-García, R., & López-Jiménez, P. A. (2008). Hydraulic Analysis of Peak Demand in Looped Water Distribution Networks. Journal of Water Resources Planning and Management, 134(6), 504-510. doi:10.1061/(asce)0733-9496(2008)134:6(504)Artita, K. S., Kaini, P., & Nicklow, J. W. (2013). Examining the Possibilities: Generating Alternative Watershed-Scale BMP Designs with Evolutionary Algorithms. Water Resources Management, 27(11), 3849-3863. doi:10.1007/s11269-013-0375-3Iglesias-Rey, P. L., Martínez-Solano, F. J., Meliá, D. M., & Martínez-Solano, P. D. (2014). BBLAWN: A Combined Use of Best Management Practices and an Optimization Model Based on a Pseudo-Genetic Algorithm. Procedia Engineering, 89, 29-36. doi:10.1016/j.proeng.2014.11.156Cheng, C.-T., Feng, Z.-K., Niu, W.-J., & Liao, S.-L. (2015). Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China. Water, 7(12), 4477-4495. doi:10.3390/w7084477Huang, Y.-C., Lin, C.-C., & Yeh, H.-D. (2015). An Optimization Approach to Leak Detection in Pipe Networks Using Simulated Annealing. Water Resources Management, 29(11), 4185-4201. doi:10.1007/s11269-015-1053-4Casillas, M., Garza-Castañón, L., & Puig, V. (2015). Optimal Sensor Placement for Leak Location in Water Distribution Networks using Evolutionary Algorithms. Water, 7(11), 6496-6515. doi:10.3390/w7116496Geem, Z. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(12), 3613-3625. doi:10.3390/w7073613Louati, M. H., Benabdallah, S., Lebdi, F., & Milutin, D. (2011). Application of a Genetic Algorithm for the Optimization of a Complex Reservoir System in Tunisia. Water Resources Management, 25(10), 2387-2404. doi:10.1007/s11269-011-9814-1SAVIC, D. A., & WALTERS, G. A. (1995). AN EVOLUTION PROGRAM FOR OPTIMAL PRESSURE REGULATION IN WATER DISTRIBUTION NETWORKS. Engineering Optimization, 24(3), 197-219. doi:10.1080/03052159508941190Nazif, S., Karamouz, M., Tabesh, M., & Moridi, A. (2009). Pressure Management Model for Urban Water Distribution Networks. Water Resources Management, 24(3), 437-458. doi:10.1007/s11269-009-9454-xCozzolino, L., Cimorelli, L., Covelli, C., Mucherino, C., & Pianese, D. (2015). An Innovative Approach for Drainage Network Sizing. Water, 7(12), 546-567. doi:10.3390/w7020546Iglesias, P. (2007). STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS. Journal of Urban and Environmental Engineering, 1(2), 61-69. doi:10.4090/juee.2007.v1n2.061069Reca, J., & Martínez, J. (2006). Genetic algorithms for the design of looped irrigation water distribution networks. Water Resources Research, 42(5). doi:10.1029/2005wr004383Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Fuertes-Miquel, V. S. (2013). Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators. Water Resources Management, 27(12), 4149-4162. doi:10.1007/s11269-013-0400-6Geem, Z. W. (2006). Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38(3), 259-277. doi:10.1080/03052150500467430Duan, 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/bf00939380Eusuff, M. M., & Lansey, K. E. (2003). Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management, 129(3), 210-225. doi:10.1061/(asce)0733-9496(2003)129:3(210)Montalvo, 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:10.1080/03052150802010607Marchi, 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.1943-5452.0000321Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Ballesteros-Pérez, P. (2015). Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing. Water Resources Management, 29(13), 4817-4831. doi:10.1007/s11269-015-1092-xElbeltagi, E., Hegazy, T., & Grierson, D. (2007). A modified shuffled frog-leaping optimization algorithm: applications to project management. Structure and Infrastructure Engineering, 3(1), 53-60. doi:10.1080/15732470500254535Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19(1), 43-53. doi:10.1016/j.aei.2005.01.004Wang, Q., Guidolin, M., Savic, D., & Kapelan, Z. (2015). Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front. Journal of Water Resources Planning and Management, 141(3), 04014060. doi:10.1061/(asce)wr.1943-5452.0000460Eiben, A. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2), 124-141. doi:10.1109/4235.771166Geem, Z. W., & Cho, Y.-H. (2011). Optimal Design of Water Distribution Networks Using Parameter-Setting-Free Harmony Search for Two Major Parameters. Journal of Water Resources Planning and Management, 137(4), 377-380. doi:10.1061/(asce)wr.1943-5452.0000130McClymont, K., Keedwell, E., & Savic, D. (2015). An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design. Environmental Modelling & Software, 69, 414-424. doi:10.1016/j.envsoft.2014.12.023Morgan, D. R., & Goulter, I. C. (1985). Optimal urban water distribution design. Water Resources Research, 21(5), 642-652. doi:10.1029/wr021i005p00642Savic, 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

Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a recently developed cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity

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.

Competent genetic-evolutionary optimization of water distribution systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe