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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2023: Evolutionary Multi-Criterion Optimization pp 29–42Cite as

RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13970))

Abstract

Multi-objective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a certain trade-off among objectives. When multiple PO solutions are to be considered for different scenarios as platform-based solutions, a common structure in them, if available, is highly desired for easier understanding, standardization, and management purposes. In this paper, we propose a modified optimization methodology to avoid converging to theoretical PO solutions having no common structure and converging to a set of near-Pareto solutions having simplistic common principles with regularity where the common principles are extracted from the PO solutions in an automated fashion. After proposing the methodology, we first demonstrate its working principle on a number of constrained and unconstrained multi-objective test problems. Thereafter, we demonstrate the practical significance of the proposed approach to a number of popular engineering design problems. Searching for a set of solutions with regularity-based principles for different platforms is a practically important task. This paper should encourage more similar algorithmic developments in the near future.

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References

  1. Singh, H.K., Isaacs, A., Nguyen, T.T., Ray, T., Yao, X.: Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems. In: 2009 IEEE Congress on Evolutionary Computation, pp. 3127–3134. IEEE (2009)

    Google Scholar 

  2. Elsayed, S., Hamza, N., Sarker, R.: Testing united multi-operator evolutionary algorithms-II on single objective optimization problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2966–2973. IEEE (2016)

    Google Scholar 

  3. Gwiazda, T.D.: Crossover for Single-Objective Numerical Optimization Problems, vol 1. Tomasz Gwiazda (2006)

    Google Scholar 

  4. Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 403–449. Springer, Boston (2014). https://doi.org/10.1007/978-1-4614-6940-7_15

    Chapter  Google Scholar 

  5. Taboada, H.A., Espiritu, J.F., Coit, D.W.: MOMS-GA: a multi-objective multi-state genetic algorithm for system reliability optimization design problems. IEEE Trans. Reliab. 57(1), 182–191 (2008)

    Article  Google Scholar 

  6. Obayashi, S., Jeong, S., Chiba, K.: Multi-objective design exploration for aerodynamic configurations. In: 35th AIAA Fluid Dynamics Conference and Exhibit, p. 4666 (2005)

    Google Scholar 

  7. Yildiz, A.R., Solanki, K.N.: Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. Int. J. Adv. Manufact. Technol. 59(1), 367–376 (2012). https://doi.org/10.1007/s00170-011-3496-y

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Mane, S., Narasinga, M.R.: Many-objective optimization: problems and evolutionary algorithms-a short review. Int. J. Appl. Eng. Res. 12(20), 9774–9793 (2017)

    Google Scholar 

  10. Cui, Z., et al.: A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Inf. Sci. 62(7), 1–3 (2019). https://doi.org/10.1007/s11432-018-9729-5

    Article  Google Scholar 

  11. Miettinen, K.: Nonlinear Multiobjective Optimization (1999)

    Google Scholar 

  12. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons Inc., USA (2001)

    MATH  Google Scholar 

  13. Deb, K., Srinivasan, A.: Innovization: innovating design principles through optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1629–1636 (2006)

    Google Scholar 

  14. Keutzer, K., Newton, A.R., Rabaey, J.M., Sangiovanni-Vincentelli, A.: System-level design: orthogonalization of concerns and platform-based design. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 19(12), 1523–1543 (2000)

    Article  Google Scholar 

  15. Sangiovanni-Vincentelli, A.: Defining platform-based design. EEDesign (2002)

    Google Scholar 

  16. Sangiovanni-Vincentelli, A., Carloni, L., De Bernardinis, F., Sgroi, M.: Benefits and challenges for platform-based design. In: Proceedings of the 41st Annual Design Automation Conference, pp. 409–414 (2004)

    Google Scholar 

  17. Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(1), 1997–2017 (2019)

    MathSciNet  MATH  Google Scholar 

  18. Gaur, A., Deb, K.: Effect of size and order of variables in rules for multi-objective repair-based innovization procedure. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2177–2184. IEEE (2017)

    Google Scholar 

  19. Ghosh, A., Goodman, E., Deb, K., Averill, R., Diaz, A.: A large-scale bi-objective optimization of solid rocket motors using innovization. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  20. Bandaru, S., Aslam, T., Ng, A.H., Deb, K.: Generalized higher-level automated innovization with application to inventory management. Eur. J. Oper. Res. 243(2), 480–496 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Mkaouer, M.W., Kessentini, M., Bechikh, S., Deb, K., Ó Cinné, M.: Recommendation system for software refactoring using innovization and interactive dynamic optimization. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, pp. 331–336 (2014)

    Google Scholar 

  22. Mittal, S., Saxena, D.K., Deb, K.: Learning-based multi-objective optimization through ANN-assisted online innovization. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 171–172 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  24. Pham, D.T., Dimov, S.S., Nguyen, C.D.: Selection of k in k-means clustering. In: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 219, no. 1, pp. 103–119 (2005)

    Google Scholar 

  25. Mittal, S., Kumar, D., Deb, S.K.: A unified automated innovization framework using threshold-based clustering. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Optimization and Innovation (COIN) Laboratory, Michigan State University, East Lansing, Michigan, 48824, USA

    Ritam Guha & Kalyanmoy Deb

Authors
  1. Ritam Guha
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  2. Kalyanmoy Deb
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Corresponding author

Correspondence to Ritam Guha .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  2. Leiden University, Leiden, The Netherlands

    André Deutz

  3. Leiden University, Leiden, The Netherlands

    Hao Wang

  4. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  5. TH Köln, Gummersbach, Germany

    Boris Naujoks

  6. University of Exeter, Exeter, UK

    Ke Li

  7. University of Jyvaskyla, Jyvaskyla, Finland

    Kaisa Miettinen

  8. De Montfort University, Leicester, UK

    Iryna Yevseyeva

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Guha, R., Deb, K. (2023). RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27249-3

  • Online ISBN: 978-3-031-27250-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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