K. Mitra, K. Deb, and S. K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):6987, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of various evolutionary approaches to multi objective optimization using six carefully chosen test functions. In this work, they found that NSGA (with elitism) proposed by Srinivas and Deb in [35] surpasses several other methods. Besides, such a method has been applied to solve various problems [23, 36]. For these reasons we opted to use such an algorithm in our study. The idea behind the NSGA is that a ranking selection method is used to emphasize good points and a niche method is used to maintain stable subpopulations of good points. It di ers from simple genetic algorithm only in the way the ....

K. Mitra, K. Deb, and S. K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):6987, 1998.

....Arti cial Intelligence, 58, 110] J. Appl. Polym. Sci. 153] J. Biomol. Struct. Dyn. 13] J. Comput. Inf. Technol. CIT (Croatia) 150] J. Ferrocem. 42] J. Inst. Electron. Telecommun. Eng. India) 74] J. Inst. Eng. India) Electr. Eng. Div. 45] Journal of Applied Polymer Science, [130] Journal of Computational Biology, 71] Journal of Hydraulic Engineering, 100] 11 12 Genetic algorithms in India Journal of Information Sciences, 136] Journal of Structural Engineering ASCE, 170, 171] Journal of the Indian Institute of Science, 90, 98, 103] Journal of Theoretical ....

....[111] Chaudhury, S. 161] Chellappan, C. 63] Chellappen, C. 47] Chockalingam, T. 36, 157, 158] Chowdhury, Nirmalya, 68] Dastidar, D. Ghosh, 28, 162, 163] Datta, Amlan, 18] De, Partha Sarathi, 144] De, S. 133] De, Susmita, 44, 110] Deb, Kalyanmoy, 18, 54, 138, 147, 164] Deb, K. [38, 109, 121, 129, 130, 142, 143, 150] Deo, Brahma, 18] Dev, Keshav, 39] Dutta, Paramartha, 61, 83] Dutta, P. 104] DuttaMajumder, Dwijesh, 61, 83] Falistagi, Abhijit, 62] Garg, Sanjeev, 154] George, Suju M. 20, 30] Ghose, S. 67] Ghosh, A. 118, 133, 150] Ghosh, Ashish, 44, 110, 136] Ghoshal, J. 65] ....

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K. Mitra, K. Deb, and K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):69-87, 5. July 1998. ga98aKMitra.

....Tsinghua Univ. Sci. Technol. China) 303, 401] J. Univ. Electron. Sci. Technol. China (China) 444] J. Water Resour Plann Manage, 1116] J. Xidian Univ. China) 179, 439] J. Zhejiang Univ. China) 349] Jisuanji Yu Yingyong Huaxue, 279, 416, 445] Journal of Applied Polymer Science, [591] Journal of Beijing University of Aeronautics and Astronautics, 284] Journal of Chemical Information and Computer Sciences, 325, 373, 381] Journal of Computational Structural Mechanics and Applications, 129] Journal of Computing in Civil Engineering, 668, 692] Journal of Hydraulic ....

....Pataya, 1229, 1235] Dangprasert, P. 1236, 1241] Danian, Zheng, 312, 339] Dastidar, D. Ghosh, 489, 624, 625] Datta, Amlan, 479] Dazhen, Ye, 183] Dazhong, Wang, 332] De, Partha Sarathi, 606] De, S. 594] De, Susmita, 505, 572] Deb, Kalyanmoy, 479, 515, 599, 609, 626] Deb, K. [499, 571, 582, 590, 591, 603, 605, 612] Dejing, Yu, 760] Deng, Bo, 257] Deo, Brahma, 479] Dequn, Liang, 232] Authors 19 Deris, Safaai, 1227] Dev, Keshav, 500] Devaney, M. J. 1115, 1162] Dhodhi, Muhammad K. 30] Di, Chang, 265] Diu, C. K. 67] Djurisic, A. B. 372] Djuri si c, Aleksandra B. 372] Dong, Cong, ....

[Article contains additional citation context not shown here]

K. Mitra, K. Deb, and K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):69-87, 5. July 1998. ga98aKMitra.

....Jiaotong Univ. China) 232] Japanese Journal of Applied Physics, Part 1, 133, 134] JIM, Materials Transactions, 339] Jisuanji Yu Yingyong Huaxue, 1183] Joho Shori, 980, 990] Journal of Applied Crystallography, 821] Journal of Applied Physics, 473] Journal of Applied Polymer Science, [238] Journal of Biomolecular NMR, 963, 288, 1116, 1164, 597, 1135] Journal of Biomolecular Structure Dynamics, 1018, 1108] Journal of Chemical Information and Computer Sciences, 1137, 38, 1147, 43, 45, 50, 279, 280, 292, 301, 63, 1163, 1139, 1013, 70, 1167, 1170, 74, 337, 438, 77, 1142, 348, ....

....Darden, Thomas A. 328] David, W. I. F. 325, 856, 872, 1182] Dawoud, M. M. 706, 218] Dawson, J. F. 500] Dean, J. P. 62] De Angelo, S. 861] Deaton, R. 599, 600, 605] Deaven, D. M. 129, 1154, 135] Deb, Kalyanmoy, 243, 687] 22 Genetic algorithms in chemistry and physics Deb, K. [238] DeChaine, Michael D. 757, 758, 760, 763] Degener, T. F. 832] DeGrado, William F. 985] Delabie, C. 503] Delaney, B. 688] de Haan, V. O. 812] Del Carpio, Carlos A. 986] Del Carpio, Carlos Adriel, 210] De Noord, Onno E. 1112] Delsanto, P. P. 21] Demiral, Birol, 874] ....

[Article contains additional citation context not shown here]

K. Mitra, K. Deb, and K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):69-87, 5. July 1998. ga98aKMitra.

....the determinism of the first non dominated front is the overall complexity of Rudolph s algorithm is also . 3 Elitist Non dominated Sorting Genetic Algorithm (NSGA II) The non dominated sorting GA (NSGA) proposed by Srinivas and Deb in 1994 has been applied to various problems [10, 7]. However as mentioned earlier there have been a number of criticisms of the NSGA. In this section, we modify the NSGA approach in order to alleviate all the above difficulties. We begin by presenting a number of different modules that form part of NSGA II. 3.1 A fast non dominated sorting ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69--87.

....by using the sharing strategy. This process continues till all solutions are assigned a shared fitness. A proportionate selection method is used with the shared fitness values. Search operators are used as usual. On a number of test problems [10] and on a number of engineering design problems [7, 11], NSGA is reported to find a number of non dominated solutions. However, NSGAs have been criticized for the following three reasons: i) the nondominated sorting approach is O(N 3 ) where N is the population size, ii) no elitism approach is used, and (iii) a sharing parameter share needs to ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69--87.

.... determinism of the first non dominated front is O(mN 2 ) the overall complexity of Rudolph s algorithm is also O(mN 2 ) 3 Elitist Non dominated Sorting Genetic Algorithm (NSGA II) The non dominated sorting GA (NSGA) proposed by Srinivas and Deb in 1994 has been applied to various problems [10, 7]. However as mentioned earlier there have been a number of criticisms of the NSGA. In this section, we modify the NSGA approach in order to alleviate all the above difficulties. We begin by presenting a number of different modules that form part of NSGA II. 3.1 A fast non dominated sorting ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69--87.

....paper, we describe the principle of multi objective optimization and then discuss a number of evolutionary algorithms. Since evolutionary algorithms deal with a population of solutions [15] it is logical that they can be used to find multiple Pareto optimal solutions in one single simulation run [3, 11, 18, 24, 1 26, 35]. We describe one such algorithm Non dominated sorting GA or NSGA [32] in somewhat greater details. We present simulation results of NSGA on two problems. Although most research on multi objective evolutionary algorithms have concentrated their efforts in developing new and efficient search ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69-- 87. 19

....multi processor system [45] G. T. Parks and I. Miller 1998 Pressurized water reactor reload design [31] S. Obayashi, S. Takahashi, and Y. Takeguchi 1998 Aircraft wing planform shape design [30] K. Mitra, K. Deb, and S. K. Gupta 1998 Dynamic optimization of an industrial nylon 6 semibatch reactor [29] D. Cvetkovic and I. Parmee 1998 Airframe design [5] 1.8 SUMMARY In this paper, we have discussed evolutionary algorithms for multi criterion optimization. By reviewing a couple of popular classical algorithms, it has been argued that there is a need for more efficient search algorithms for ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69--87.

....Fleming, 1993; Horn, Nafploitis, and Goldberg, 1994; Srinivas and Deb, 1994) emerged. Later, a number of other researchers have used these implementations in various multi objective optimization applications with success (Cunha, Oliviera, and Covas, 1997; Eheart, Cieniawski, and Ranjithan, 1993; Mitra, Deb, and Gupta, 1998; Parks and Miller, 1998; Weile, Michelsson, and Goldberg, 1996) A number of studies have also concentrated in developing new and improved GA implementations (Fonseca and Fleming, 1998; Leung et al. 1998; Kursawe, 1990; Laumanns, Rudolph, and Schwefel, 1998; Zitzler and Thiele, 1998a) Fonseca ....

Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial Nylon 6 semibatch reactor using genetic algorithms. Journal of Applied Polymer Science, 69(1), 69--87.

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K. Mitra, K. Deb, and S. K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):6987, 1998.

No context found.

K. Mitra, K. Deb and S. K. Gupta, "Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithms," J. Appl. Polym. Sci. 69, 1 (1998) 69--87.

No context found.

K. Mitra, K. Deb, and S. K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):6987, 1998.

No context found.

K. Mitra, K. Deb, and S. K. Gupta. Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1):6987, 1998.

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