This paper proposes a new version of the Particle Swarm Optimization (PSO) namely, Modified PSO (MPSO) for model order formulation of Single Input Single Output (SISO) linear time invariant continuous systems. In the General PSO, the movement of a particle is governed by three behaviors namely inertia, cognitive and social. The cognitive behavior helps the particle to remember its previous visited best position. In Modified PSO technique split the cognitive behavior into two sections like previous visited best position and also previous visited worst position. This modification helps the particle to search the target very effectively. MPSO approach is proposed to formulate the higher order model. The method based on the minimization of error between the transient responses of original higher order model and the reduced order model pertaining to the unit step input. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed method is illustrated through numerical example from literature., {"references":["Z. Qian and Z. Meng, \"Low order approximation for analog simulation\nof thermal processes\", ACTA Automarica Sinica (in Chinese), Vol. 4,\nNo.1, pp. 1-17. 1966.","C.F. Chen and L.S. Shien, \"A novel approach to linear model\nsimplification\", International Journal of Control System, Vol. 8, pp.\n561-570, 1968.","V. Zaliin, \"Simplification of linear time-invariant system by moment\napproximation\", International Journal of Control System, Vol. 1, No. 8,\npp. 455-460, 1973.","H. Xiheng, \"Frequency-fitting and Pade-order reduction\", Information\nand Control, Vol. 12, No. 2, 1983.","An investigation on the methodology and technique of model reduction\n(in Preprints), 7th IFAC Symposium on Identification and System\nParameter and Estimation, Vol. 2, pp.1700-1706, 1985.","P. O. Gutman, C. F. Mannerfelt and P. Molander, \"Contributions to the\nmodel reduction problem\", IEEE Trans. Auto. Control, Vol. 27, pp. 454-\n455, 1982.","J. Pal, \"System reduction by mixed method\", IEEE Transaction on\nAutomatic Control, Vol. 25, No. 5, pp. 973-976, 1980.","Y. Shamash, \"Truncation method of reduction: a viable alternative\",\nElectronics Letters, Vol. 17, pp. 97-99, 1981.","D. E. Goldberg, \"Genetic Algorithms in Search, Optimization, and\nMachine Learning\", Addison-Wesley, 1989.\n[10] M.Gopal , \"Control systems principle and design\", Tata McGraw Hill\nPublications, New Delhi, 1997.\n[11] R. C Eberhart and Y. Shi, \"Particle Swarm Optimization: Developments\napplications and resourses\", Proceedings Congress on Evolutionary\nComputation IEEE service, NJ, Korea, 2001.\n[12] A. M. Abdelbar and S. Abdelshahid, \"Swarm Optimization with instinct\ndriven particles\", Proceedings of the IEEE Congress on Evolutionary\nComputation, pp. 777-782, 2003.\n[13] U. Baumgartner, C. Magele and W. Reinhart\", Pareto optimality and\nparticle swarm optimization\" IEEE Transaction on Magnetics, Vol. 40,\npp.1172-1175, 2004.\n[14] S. N. Sivanandam and S. N. Deepa,\" A Genetic Algorithm and Particle\nSwarm Optimization approach for lower order modeling of linear time\ninvariant discrete systems \"Int. Conf. on Comp. Intelligent and\nMultimedia Application, Vol. 1, pp. 443- 447, Dec. 2007.\n[15] A. Immanuel selvakumar and K. Thanushkodi \"A New Particle Swarm\nOptimization solution to nonconvex economic dispath problem\", IEEE\nTrans. On Power System, Vol. 22. No. 1, pp. 42- 51, Feb. 2007.\n[16] R. Prasad and J. Pal, \"Stable reduction of linear systems by continued\nfractions\", Journal of Institution of Engineers IE(I) Journal, Vol. 72,\npp. 113-116, October, 1991.\n[17] Y. Shamash, \"Linear system reduction using Pade approximation to\nallow retention of dominant modes\", Int. J. Control, Vol. 21, No. 2, pp.\n257-272, 1975.\n[18] S. Mukherjee, Satakshi and R. C. Mittal, \"Model order reduction using\nresponse-matching technique\", Journal of Franklin Inst., Vol. 342 , pp.\n503-519, 2005.\n[19] S. K. Tomar and R. Prasad, \"Conventional and PSO based approaches\nfor Model order reduction of SISO Discrete systems\", International\njournal of electrical and electronics Engineering, Vol. 2, pp. 45-50,\n2009.\n[20] S. Yadav, N. P. Patidar, J. Singhai, S. Panda and C. Ardil, \"A combined\nconventional and differential evolution method for model order\nreduction\", International Journal of Computational Intelligence, Vol. 5,\nNo. 2, pp. 111-118, 2009.\n[21] S. Panda, S. K. Tomar, R. Prasad and C. Ardil, \"Reduction of linear time\ninvariant systems using Routh - approximation and PSO\", International\nJournal of Applied Mathematics and Computer Science, Vol. 5, No. 2,\npp. 82- 89, 2009.\n[22] S. Panda, S. K. Tomar, R. Prasad and C. Ardil, \"Model reduction of\nlinear systems by conventional and evolutionary techniques\",\nInternational Journal of Computational and Mathematical Science,\nVol.3, No.1, pp. 28-34, 2009."]}