A Novel DWT-CT approach in Digital Watermarking using PSO

The importance of watermarking is dramatically enhanced due to the promising technologies like Internet of Things (IoT), Data analysis, and automation of identification in many sectors. Due to these reasons, systems are inter-connected through networking and internet and huge amounts of information is generated, distributed and transmitted over the World Wide Web. Thus authentication of the information is a challenging task. The algorithm developed for the watermarking needs to be robust against various attack such as salt & peppers, filtering, compression and cropping etc. This paper focuses on the robustness of the algorithm by using a hybrid approach of two transforms such as Contourlet, Discrete Wavelet Transform (DWT). Also, the Particle Swarm Optimization (PSO) is used to optimize the embedding strength factor. The proposed digital watermarking algorithm has been tested against common types of image attacks. Experiment results for the proposed algorithm gives better performance by using similarity metrics such as NCC (Normalized Cross Correlation value) and PSNR (Peak Signal to Noise Ratio)

PSO Based Lossless and Robust Image Watermarking using Integer Wavelet Transform

In recent days, the advances in the broadcasting of multimedia contents in digital format motivate to protect this digital multimedia content form illegal use, such as manipulation, duplication and redistribution. However, watermarking algorithms are designed to meet the requirements of different applications, because, various applications have various requirements. This paper intends to design a new watermarking algorithm with an aim of provision of a tradeoff between the robustness and imperceptibility and also to reduce the information loss. This approach applies Integer Wavelet Transform (IWT) instead of conventional floating point wavelet transforms which are having main drawback of round of error. Then the most popular artificial intelligence technique, particle swarm optimization (PSO) used for optimization of watermarking strength. The strength of watermarking technique is directly related to the watermarking constant alpha. The PSO optimizes alpha values such that, the proposed approach achieves better robustness over various attacks and an also efficient imperceptibility. Numerous experiments are conducted over the proposed approach to evaluate the performance. The obtained experimental results demonstrates that the proposed approach is superior compared to conventional approach and is able to provide efficient resistance over Gaussian noise, sal

Simulation-optimization models for the dynamic berth allocation problem

Container terminals are designed to provide support for the continuous changes in container ships. The most common schemes used for dock management are based on discrete and continuous locations. In view of the steadily growing trend in increasing container ship size, more flexible berth allocation planning is mandatory. The consideration of continuous location in the container terminal is a good option. This paper addresses the berth allocation problem with continuous dock, which is called dynamic berth allocation problem (DBAP). We propose a mathematical model and develop a heuristic procedure, based on a genetic algorithm, to solve the corresponding mixed integer problem. Allocation planning aims to minimise distances travelled by the forklifts and the quay crane, for container loading and unloading operations for each ship, according to the quay crane scheduling. Simulations are undertaken using Arena software, and experimental analysis is carried out for the most important container terminal in Spain

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems

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.

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.

Tahap pengawalan pihak pengurusan politeknik dalam mengurangkan gejala sosial di kalangan pelajar

Kebelakangan ini gejala sosial di kalangan pelajar sarna ada di sekolah mahupun di Institusi Pengajian Tinggi semakin membimbangkan kita. Benta-berita dan laporan media massa dan elektronik tentang gejala sosial seperti berdua-duaan (coupling), herpeleseran, bersekedudukan, membuang bayi dan pergaulan bebas kemp kah dilaporkan dan semacam sudah menjadi perkara biasa Oleh itu kajian ini dijalankan bertujuan untuk mengenalpasti tahap pengawalan dan tahap kejayaan pengawalan yang dilakukan oleh pihak pengurusan politeknik dalam menangani gejala sosia1 di kalangan pelajar. Kajian ini dijalankan di Politeknik Kota Bharu dan 100 orang pensyarahnya dikenalpasti sebagai responden kajian. Data-data dianalisis menggunakan perisian Statistical Package of Social Science (.SPSS) Versi 10.0 melibatkan peratusan dan purata min. Dapatan kajian mendapati bahawa pihak pengurusan politeknik sudah melakukan pengawalan yang tinggi dalam menangani masalah ini. Narnun mungkin disebabkan faktor-faktor di luar kawalan maka gejala sosial dllihat masih berlaku di politeknik walaupun ianya tidaklah berapa sen us