A Novel Restart Strategy for Solving Complex Multi-modal Optimization Problems Using Real-Coded Genetic Algorithm

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Genetic algorithm (GA) is one of the most popular and robust stochastic optimization tools used in various fields of research and industrial applications. It had been applied for solving many global optimization problems for the last few decades. However, it has a poor theoretical assurance to reach the globally optimal solutions, while solving the complex multi-modal problems. Restart strategy plays an important role in overcoming this limitation of a GA to a certain extent. Although there are a few restart methods available in the literature, these are not adequate. In this paper, a novel restart strategy is proposed for solving complex multi-modal optimization problems using a real-coded genetic algorithm (RCGA). To show the superiority of the proposed scheme, ten complex multi-modal test functions have been selected from the CEC 2005 benchmark functions and its results are compared with that of the other strategies.

Keywords

Restart strategy Real-coded genetic algorithm  CEC 2005 benchmark test functions Multi-modal optimization problems 

References

  1. 1.
    Liberti, L., Kucherenko, S.: Comparison of deterministic and stochastic approaches to global optimization. Int. Trans. Oper. Res. 12(3), 263–285 (2005)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13(11), 2467–2474 (2003)CrossRefGoogle Scholar
  3. 3.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  4. 4.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  5. 5.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)Google Scholar
  7. 7.
    Yang, X.-S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)MATHGoogle Scholar
  8. 8.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Huang, J., Dong, W.S., Yan, J.C., Tian, C.H., Li, M., Mo, W.T.: Two-stage based ensemble optimization framework for large-scale global optimization. Eur. J. Oper. Res. 228(2), 308–320 (2013)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Ng, C.-K., Li, D.: Test problem generator for unconstrained global optimization. Comput. Oper. Res. 51(Suppl. C), 338–349 (2014)Google Scholar
  11. 11.
    dos Santos Coelho, L., Ayala, H.V.H., Mariani, V.C.: A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization. Appl. Math. Comput. 234(Suppl. C), 452–459 (2014)Google Scholar
  12. 12.
    Boender, C.G.E., Romeijin, H.E.: Stochastic methods. In: Horst, R., Pardalos, P.M. (eds.) Handbook of Global Optimization. Kluwer Academic Publishers, Boston (1995)Google Scholar
  13. 13.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore, May 2005 and KanGAL Report 2005, IIT Kanpur, India (2005)Google Scholar
  14. 14.
    Ghannadian, F., Alford, C., Shonkwiler, R.: Application of random restart to genetic algorithms. Inf. Sci. 95(1), 81–102 (1996)CrossRefGoogle Scholar
  15. 15.
    Beligiannis, G.N., Tsirogiannis, G.A., Pintelas, P.E.: Restartings: a technique to improve classic genetic algorithms’ performance. In: International Conference on Computational Intelligence 2004, pp. 404–407 (2004)Google Scholar
  16. 16.
    Hughes, J.A., Houghten, S., Ashlock, D.: Recentering and restarting a genetic algorithm using a generative representation for an ordered gene problem. Int. J. Hybrid Intell. Syst. 11(4), 257–271 (2014)CrossRefGoogle Scholar
  17. 17.
    Dao, S.D., Abhary, K., Marian, R.: An improved structure of genetic algorithms for global optimisation. Prog. Artif. Intell. 5(3), 155–163 (2016)CrossRefGoogle Scholar
  18. 18.
    Suksut, K., Kerdprasop, K., Kerdprasop, N.: Support vector machine with restarting genetic algorithm for classifying imbalanced data. Int. J. Futur. Comput. Commun. 6(3), 92 (2017)Google Scholar
  19. 19.
    Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)MathSciNetGoogle Scholar
  20. 20.
    Agrawal, R.B., Deb, K.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)MathSciNetMATHGoogle Scholar
  21. 21.
    Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Comput. Sci. inf. 26, 30–45 (1996)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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