{"references": ["UNESCO (2004, may 11). Water for - water for life - the united nations\nworld water development report. [Online]. Available:\nhttp://www.unesco.org/water/wwap/wwdr/ex_summary/", "M.S. Mohsen, 0.R AI-Jayyousi, \"Brackish water desalination: an\nalternative for water supply enhancement in Jordan--, Desalination,\nvol.124, p~163-174, Nov.1999", "A. Maurel, \"Desalination of sea Water and brackish water,\" in Proc. Of\nseminar on water managemen6 strategies in Mediterranean countries", "F. G. Shinskey, Process Control System: Application, Design and Tuning.\nMcGraw-Hill, 4th ed, 1996", "K. J. Astrom and B. Wittenmark, Adaptive Control. Addison Wesley, 2nd\ned., 1995.", "A. Visioli, \"Tuning of PID controllers with fuzzy logic,\" Proc. Inst. Elect.\nEng. Contr. Theory Applicat., vol. 148, no. 1, pp. 1-8, Jan. 2001.", "R. A. Krohling and J. P. Rey, \"Design of optimal disturbance rejection\nPID controllers using genetic algorithm,\" IEEE Trans. Evol. Comput., vol.\n5, pp. 78-82, Feb. 2001.", "Zwe-Lee Gaing, \"A Particle Swarm Optimization Approach for Optimum\nDesign of PID Controller in AVR System,\" IEEE TRANSACTIONS ON\nENERGY CONVERSION, VOL. 19, NO. 2, pp.384~391, 2004.", "Dionisio S. Pereira, \"Genetic Algorithm Based System Identification and\nPID Tuning for Optimum Adaptive Control,\" International Conference\non Advanced Intelligent Mechatronics, Monterey, California, USA, 24-28\nJuly, pp.801~806, 2005\n[10] Ian Griffin, \"On-line PID Controller Tuning using Genetic Algorithms,\"\nDublin City University, 2003\n[11] T O.Mahony, C J Downing and K Fatla, \"Genetic Algorithm for PID\nParameter Optimization: Minimizing Error Criteria,\" Process Control and\nInstrumentation 2000 26-28 July 2000, University of Stracthclyde,\npp.148~153\n[12] C. R. Houck, J. Joines. and M.Kay, \"A genetic algorithm for function\noptimization: A Matlab implementation,\" ACM Transactions on\nMathematical Software, 1996.\n[13] Chipperfield, A. J., Fleming, P. J., Pohlheim, H. and Fonseca, C. M., A\n\"Genetic Algorithm Toolbox for MATLAB,\" Proc. International\nConference on Systems Engineering, Coventry, UK, 6-8 September, 1994\n[14] D. E. Goldberg, \"Genetic Algorithms in Search, Optimization, and\nMachine Learning,\" Addison-Wesley Publishing Co., Inc., 1989\n[15] Wikipedia, http://en.wikipedia.org/wiki/Cauchy_distribution\n[16] Stephen K. Park and Keith W. Miller. Random number generators: Good\nones are hard to find. CACM, 31(10):1192-1201, 1988.\n[17] Donald E. Knuth. \"Deciphering a linear congruential encryption,\". IEEE\nTransactions on Information Theory, IT-31(1):49-52, January 1985.\n[18] T. K. Teng, J. S. Shieh and C. S. Chen, \"Genetic algorithms applied in\nonline autotuning PID parameters of a liquid-level control system,\"\nTransaction of the Institute of Measurement and control 25, 5 (2003),\npp.433~450"]}

An optimal control of Reverse Osmosis (RO) plant is studied in this paper utilizing the auto tuning concept in conjunction with PID controller. A control scheme composing an auto tuning stochastic technique based on an improved Genetic Algorithm (GA) is proposed. For better evaluation of the process in GA, objective function defined newly in sense of root mean square error has been used. Also in order to achieve better performance of GA, more pureness and longer period of random number generation in operation are sought. The main improvement is made by replacing the uniform distribution random number generator in conventional GA technique to newly designed hybrid random generator composed of Cauchy distribution and linear congruential generator, which provides independent and different random numbers at each individual steps in Genetic operation. The performance of newly proposed GA tuned controller is compared with those of conventional ones via simulation.