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A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results

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Evolutionary Multi-Criterion Optimization (EMO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9018))

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Abstract

Most evolutionary multi-objective optimization (EMO) methods use domination and niche-preserving principles in their selection operation to find a set of Pareto-optimal solutions in a single simulation run. However, classical generative multi-criterion optimization methods repeatedly solve a parameterized single-objective problem to achieve the same. Due to lack of parallelism in the classical generative methods, they have been reported to be slow compared to efficient EMO methods. In this paper, we use a specific scalarization method, but instead of repetitive independent applications, we formulate a multimodal scalarization of multiple objectives and develop a niche-based evolutionary algorithm to find multiple Pareto-optimal solutions in a single simulation run. Proof-of-principle results on two to 10-objective problems from our proposed multimodal approach are compared with standard evolutionary multi/many-objective optimization methods.

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References

  1. Cavicchio, D.J.: Adaptive Search Using Simulated Evolution. PhD thesis: University of Michigan, Ann Arbor (1970)

    Google Scholar 

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

    MATH  Google Scholar 

  3. Coello, C.A.C., VanVeldhuizen, D.A., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, Boston (2002)

    Book  MATH  Google Scholar 

  4. Das, I., Dennis, J.E.: Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal of Optimization 8(3), 631–657 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  5. Das, S., Maity, S., Qu, B.-Y., Suganthan, P.N.: Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art. Swarm and Evolutionary Computation 1(2), 71–88 (2011)

    Article  Google Scholar 

  6. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

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

    MATH  MathSciNet  Google Scholar 

  8. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 42–50 (1989)

    Google Scholar 

  10. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18(4), 577–601 (2014)

    Article  Google Scholar 

  11. Deb, K., Saha, A.: Multimodal optimization using a bi-objective evolutionary algorithms. Evolutionary Computation Journal 20(1), 27–62 (2012)

    Article  Google Scholar 

  12. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization, pp. 105–145. Springer, London (2005)

    Chapter  Google Scholar 

  13. DeJong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis. University of Michigan, Ann Arbor (1975). Dissertation Abstracts International 36(10), 5140B (University Microfilms No. 76–9381)

    Google Scholar 

  14. Ehrgott, M.: Multicriteria Optimization. Springer, Berlin (2000)

    Book  MATH  Google Scholar 

  15. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423, Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  16. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 41–49 (1987)

    Google Scholar 

  17. Harik, G.: Finding multi-modal solutions using restricted tournament selection. In: Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), pp. 24–31 (1997)

    Google Scholar 

  18. Horn, J., Nafploitis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multi-objective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)

    Google Scholar 

  19. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)

    MATH  Google Scholar 

  20. Petrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: Proceedings of Third IEEE International Conference on Evolutionary Computation ICEC 1996, pp. 798–803. IEEE Press, Piscataway (1996)

    Chapter  Google Scholar 

  21. Shukla, P., Deb, K.: Comparing classical generating methods with an evolutionary multi-objective optimization method. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 311–325. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  22. Srinivas, N., Deb, K.: Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation Journal 2(3), 221–248 (1994)

    Article  Google Scholar 

  23. Tutum, C.C., Deb, K.: A multimodal approach for evolutionary multi-objective optimization: MEMO. COIN Report Number 2014018, Computational Optimization and Innovation Laboratory (COIN), Electrical and Computer Engineering, Michigan State University, East Lansing (2014)

    Google Scholar 

  24. Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Applications, pp. 468–486. Springer, Berlin (1980)

    Chapter  Google Scholar 

  25. Yin, X., Germay, N.: A fast genetic algorithm with sharing scheme using clustering analysis methods in multimodal function optimization. In: Proceedings of International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 450–457 (1993)

    Google Scholar 

  26. Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11(6), 712–731 (2007)

    Article  Google Scholar 

  27. 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. Technical report, Nanyang Technological University, Singapore (2008)

    Google Scholar 

  28. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Michigan State University, 428 S. Shaw Lane, 2120 EB, East Lansing, MI, 48824, USA

    Cem C. Tutum & Kalyanmoy Deb

Authors
  1. Cem C. Tutum
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  2. Kalyanmoy Deb
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Correspondence to Kalyanmoy Deb .

Editor information

Editors and Affiliations

  1. Institute for Polymers and Composites/I3N,University of Minho, Guimaraes, Portugal

    António Gaspar-Cunha

  2. Dept. of Electrical and Computer Engg, University of Coimbra, Coimbra, Portugal

    Carlos Henggeler Antunes

  3. CINVESTAV-IPN Depto. de Computacíon, Col. San Pedro Zacatenco, México DF, Mexico

    Carlos Coello Coello

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Tutum, C.C., Deb, K. (2015). A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-15934-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15933-1

  • Online ISBN: 978-3-319-15934-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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