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Implementation and Application of NSGA-III Improved Algorithm in Multi-objective Environment

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

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Abstract

Recently, the development of evolutionary multi-objective optimization (EMO) algorithm to deal with multi-objective optimization problems (with four or more objectives) has gradually become a hot spot. NSGA-III algorithm is effective in dealing with evolutionary multi-objective optimization problems. In this paper, we recognize some advantages of the existing NSGA-III algorithm and make some improvements. The improved NSGA-III algorithm has higher adaptability and can provide more dense Pareto-optimal front under the same amount of computation. The improved NSGA-III algorithm is applied to many multi-objective testing problems with 3 to 8 objectives, and its performance is compared with the existing multi-objective evolutionary algorithms. Experimental results show that the improved algorithm can produce satisfactory results for all the problems considered in this study. Among the 28 environments with all values, the improved NSGA-III algorithm has 22 optimal values, accounting for 78.57%. After that, we analyze the results and put forward the future improvement and research direction.

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References

  1. Chikumbo, O., Goodman, E., Deb, K.: Approximating a multidimensional pareto front for a land use management problem: a modifified MOEA with an epigenetic silencing metaphor. In: Proceedings of Congress on Evolutionary Computation (CEC-2012), pp. 1–8 (2012)

    Google Scholar 

  2. Coello, C.A.C., Lamont, G.B.: Applications of Multi-Objective Evolutionary Algorithms. World Scientifific, Singapore (2004)

    Book  Google Scholar 

  3. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  4. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

  6. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  7. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., Tsahalis, D.T., P´eriaux, J., Papailiou, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems. Athens, Greece: International Center for Numerical Methods in Engineering (CIMNE), pp. 95–100 (2001)

    Google Scholar 

  8. Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)

    Article  Google Scholar 

  9. Deb, K., Jain, H.: An improved NSGA-II procedure for manyobjective optimization Part I: Problems with box constraints. Indian Institute of Technology Kanpur, Technical report, 2012009 (2012)

    Google Scholar 

  10. Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. North-Holland, New York (1983)

    MATH  Google Scholar 

  11. Garza-Fabre, M., Pulido, G.T., Coello, C.A.C.: Ranking methods for many-objective optimization. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds.) MICAI 2009. LNCS (LNAI), vol. 5845, pp. 633–645. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05258-3_56

    Chapter  Google Scholar 

  12. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10(3), 263–282 (2002)

    Article  Google Scholar 

  13. Hadka, D., Reed, P.: Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol. Comput. J., in press

    Google Scholar 

  14. Farina, M., Amato, P.: A fuzzy defifinition of ‘optimality’ for manycriteria decision-making and optimization problems. IEEE Trans. Syst. Man Cybern. 34(3), 315–326 (2004)

    Article  Google Scholar 

  15. Zou, X., Chen, Y., Liu, M., Kang, L.: A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 38(5), 1402–1412 (2008)

    Google Scholar 

  16. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  17. Sato, H., Aguirre, E., Tanaka, K.: Pareto partial dominance moea in many-objective optimization. In: Proceedings of Congress on Computational Intelligence (CEC-2010), 2010, pp. 1–8

    Google Scholar 

  18. Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  19. Deb, K., Jain, H.: Handling many-objective problems using an improved NSGA-II procedure. In: Proceedings of World Congress on Computational Intelligence (WCCI-2012), pp. 1–8 (2012)

    Google Scholar 

  20. Das, I., Dennis, J.: Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)

    Article  MathSciNet  Google Scholar 

  21. Xue, F., Wu, D.: NSGA-III algorithm with maximum ranking strategy for many-objective optimisation. Int. J. Bio-Inspired Comput. 15(1), 14–23 (2020)

    Article  Google Scholar 

  22. Essiet, I.O., Sun, Y., Wang, Z.: Improved genetic algorithm based on particle swarm optimization-inspired reference point placement. Eng. Optimizat. 51(7), 1097–1114 (2019)

    Article  MathSciNet  Google Scholar 

  23. Zhang, Q.: MOEA/D. http://dces.essex.ac.uk/staff/zhang/webofmoead.htm

  24. Veldhuizen, D.V., Lamont, G.B.: Multiobjective evolutionary algorithm research: a history and analysis. Dayton, O.H. (ed.) Department of Electrical and Computer Engineering, Air Force Institute of Technology, Technical report TR-98–03 (1998)

    Google Scholar 

  25. Zhang, Q., Zhou, A., Zhao, S.Z., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the cec-2009 special session and competition. Singapore: Nanyang Technological University, Technical report (2008)

    Google Scholar 

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Authors and Affiliations

  1. Beijing Wuzi University, Beijing, 101149, China

    Fei Xue, Yuelu Gong, Qiuru Hai & Huilin Qin

  2. Beijing University of Technology, Beijing, 100124, China

    Tingting Dong

Authors
  1. Fei Xue
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  2. Yuelu Gong
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  3. Qiuru Hai
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  4. Huilin Qin
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  5. Tingting Dong
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Corresponding author

Correspondence to Yuelu Gong .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Xue, F., Gong, Y., Hai, Q., Qin, H., Dong, T. (2022). Implementation and Application of NSGA-III Improved Algorithm in Multi-objective Environment. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_10

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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