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Jumping Gene Adaptations of NSGA-II with Altruism Approach: Performance Comparison and Application to Williams–Otto Process

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Applications of Metaheuristics in Process Engineering

Abstract

Elitist non-dominated sorting genetic algorithm (NSGA-II) has been widely used for solving many application problems with multiple objectives. The concept of jumping gene (JG) from natural genetics has been incorporated into NSGA-II to improve its performance. Several JG adaptations have been proposed and used to solve multi-objective optimization test/application problems; aJG, saJG and sJG are recent JG adaptations, and they have similar performance. Further, the concept of altruism, inspired by the honey bee colony, has been incorporated with NSGA-II-aJG, and it has improved the search performance. In the present work, Alt-NSGA-II-aJG is modified for using saJG and sJG adaptations, and then performances of Alt-NSGA-II-aJG, Alt-NSGA-II-saJG and Alt-NSGA-II-sJG algorithms are compared on test and application problems. In the literature, the maximum number of generations (MNG) is the commonly used termination criterion for stochastic search algorithms. Hence, a search termination criterion based on the improvement in the Pareto-optimal front obtained has been included in the present study. Performance of selected algorithms is compared using both improvement-based termination criterion and MNG; here, generational distance, spread and inverse generational distance are employed to assess the quality of non-dominated solutions obtained. Results show that performance of Alt-NSGA-II-aJG, Alt-NSGA-II-saJG and Alt-NSGA-II-sJG algorithms is comparable, and use of the altruism approach and improvement-based termination criterion enhances the search algorithm significantly.

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References

  1. Agarwal, A., Gupta, S.K.: Jumping gene adaptations of NSGA-II and their use in the multi-objective optimal design of shell and tube heat exchangers. Chem. Eng. Res. Design 86(2), 123–139 (2008)

    Article  MathSciNet  Google Scholar 

  2. Agrawal, N., Rangaiah, G., Ray, A.K., Gupta, S.K.: Design stage optimization of an industrial low-density polyethylene tubular reactor for multiple objectives using NSGA-II and its jumping gene adaptations. Chem. Eng. Sci. 62(9), 2346–2365 (2007)

    Article  Google Scholar 

  3. Agrawal, N., Rangaiah, G.P., Ray, A.K., Gupta, S.K.: Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Ind. Eng. Chem. Res. 45(9), 3182–3199 (2006)

    Article  Google Scholar 

  4. Azzaro-Pantel, C., Ouattara, A., Pibouleau, L.: Ecodesign of chemical processes with multi-objective genetic algorithms, pp. 335–367, in Rangaiah G.P. and Bonilla-Petriciolet A. (Editors), Multi-Objective Optimization in Chemical Engineering: Developments and Applications, Wiley, New York (2013)

    Google Scholar 

  5. Bhat, S.A.: On-line optimizing control of bulk free radical polymerization of methyl methacrylate in a batch reactor using virtual instrumentation. Ph.D. Thesis. Indian Institute of Technology, Kanpur (2007)

    Google Scholar 

  6. Chaudhari, P., Gupta, S.K.: Multiobjective optimization of a fixed bed maleic anhydride reactor using an improved biomimetic adaptation of NSGA-II. Ind. Eng. Chem. Res. 51(8), 3279–3294 (2012)

    Article  Google Scholar 

  7. Coello Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Berlin/Heidelberg (2007)

    MATH  Google Scholar 

  8. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  9. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2000)

    Article  Google Scholar 

  10. Deb, K., Agarwal, S., Pratap A. and Meyarivan T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. Technical Report 2000001. Indian Institute of Technology, Kanpur (2000)

    Google Scholar 

  11. Guria, C., Verma, M., Mehrotra, S.P., Gupta, S.K.: Multi-objective optimal synthesis and design of froth flotation circuits for mineral processing, using the jumping gene adaptation of genetic algorithm. Ind. Eng. Chem. Res. 44(8), 2621–2633 (2005)

    Article  Google Scholar 

  12. Kasat, R.B., Gupta, S.K.: Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator. Comput. Chem. Eng. 27(12), 1785–1800 (2003)

    Article  Google Scholar 

  13. Lee, S.Q.L., Rangaiah, G.P., Agrawal, N.: Optimal design of chemical processes for multiple economic and environmental objectives, pp. 301–338, in Rangaiah G.P. (Editor), Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, Vol. 1 in the Advances in Process Systems Engineering, World Scientific, Singapore (2009)

    Google Scholar 

  14. Masuduzzaman, Rangaiah, G.P.: Multi-objective optimization applications in chemical engineering, pp. 27–52, in Rangaiah G.P. (Editor), Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, Vol. 1 in the Advances in Process Systems Engineering, World Scientific, Singapore (2009)

    Google Scholar 

  15. McClintock, B.: The discovery and characterization of transposable elements. In: the Collected Papers of Barbara McClintock. Garland, New York (1987)

    Google Scholar 

  16. Nabavi, S.R., Rangaiah, G.P., Niaei, A., Salari, D.: Multiobjective optimization of an industrial lpg thermal cracker using a first principles model. Ind. Eng. Chem. Res. 48(21), 9523–9533 (2009)

    Article  Google Scholar 

  17. Nabavi, S.R., Rangaiah, G.P., Niaei, A., Salari, D.: Design optimization of an LPG thermal cracker for multiple objectives. Int. J. Chem. Reactor Eng. 9(1) (2011)

    Google Scholar 

  18. Ramteke, M., Gupta, S.K.: Biomimetic adaptation of the evolutionary algorithm, NSGA-II-aJG, using the biogenetic law of embryology for intelligent optimization. Ind. Eng. Chem. Res. 48(17), 8054–8067 (2009)

    Article  Google Scholar 

  19. Ramteke, M., Gupta, S.K.: Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm. Ind. Eng. Chem. Res. 48(21), 9671–9685 (2009)

    Article  Google Scholar 

  20. Ramteke, M., Gupta, S.K.: Multi-objective genetic algorithm and simulated annealing with jumping gene adaptations, pp. 91–129, in Rangaiah G.P. (Editor), Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, Vol. 1 in the Advances in Process Systems Engineering, World Scientific, Singapore (2009)

    Google Scholar 

  21. Ripon, K.S.N., Kwong, S., Man, K.: A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization. Inform. Sci. 177(2), 632–654 (2007)

    Article  MATH  Google Scholar 

  22. Sharma, S., Nabavi, S.R., Rangaiah, G.P.: Performance Comparison of Jumping Gene Adaptations of the Elitist Non-dominated Sorting Genetic Algorithm, pp. 103–127, in Rangaiah G.P. and Bonilla-Petriciolet A. (Editors), Multi-Objective Optimization in Chemical Engineering: Developments and Applications, Wiley, New York (2013)

    Google Scholar 

  23. Sharma, S., Rangaiah, G.P.: An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput. Chem. Eng. 56, 155–173 (2013)

    Article  Google Scholar 

  24. Sharma, S., Rangaiah, G.P.: Multi-objective optimization applications in chemical engineering, pp. 35–102, in Rangaiah G.P. and Bonilla-Petriciolet A. (Editors), Multi-Objective Optimization in Chemical Engineering: Developments and Applications, Wiley, New York (2013)

    Google Scholar 

  25. Sindhya, K., Deb, K., Miettinen, K.: A local search based evolutionary multi-objective optimization approach for fast and accurate convergence, in Rudolph, G., Jansen, T., Lucas, S., Poloni, C. and Beume, N. (editors), Lecture Notes in Computer Science 5199, 815–824, Springer, Berlin/Heidelberg (2008)

    Google Scholar 

  26. Srinivas, N., Deb, K.: Multiobjective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1994)

    Article  Google Scholar 

  27. Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary computation and convergence to a pareto front. In: Stanford University, California, pp. 221–228. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  28. Williams, T.J., Otto, R.E.: A generalized chemical processing model for the investigation of computer control. Trans. Am. Inst. Electr. Eng. I Commun. Electron. 79(5), 458–473 (1960)

    Google Scholar 

  29. Zhang, Q., Zhou, A., Zhano, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multi-objective optimization test instances for the CEC 2009 special session and competition. In: CEC Special Session on the Performance Assessment of Multi-objective Optimization Algorithms (2009)

    Google Scholar 

  30. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 3234–3241. IEEE (2006)

    Google Scholar 

  31. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8, 173–195 (2000)

    Article  Google Scholar 

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Acknowledgement

The authors are grateful for the financial support provided by the Public Utilities Board, Singapore, for the research reported in this book chapter.

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Authors and Affiliations

  1. Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore

    Shivom Sharma & G. P. Rangaiah

  2. Faculty of Chemistry, University of Mazandaran Babolsar, Babolsar, Iran, 47415

    S. R. Nabavi

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  1. Shivom Sharma
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  2. S. R. Nabavi
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  3. G. P. Rangaiah
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Correspondence to G. P. Rangaiah .

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Editors and Affiliations

  1. Evol. Comput. & Image Proc. Group, Center for Develop. of Adv. Computing, Pune, India

    Jayaraman Valadi

  2. Lab. LiSSi (EA 3956), Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Sharma, S., Nabavi, S.R., Rangaiah, G.P. (2014). Jumping Gene Adaptations of NSGA-II with Altruism Approach: Performance Comparison and Application to Williams–Otto Process. In: Valadi, J., Siarry, P. (eds) Applications of Metaheuristics in Process Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06508-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-06508-3_17

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