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An Improved Opposition Based Grasshopper Optimisation Algorithm for Numerical Optimization

  • Divya BairathiEmail author
  • Dinesh Gopalani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

In this paper an improved optimization algorithm called Opposition Based Grasshopper Optimisation Algorithm (OGOA) is proposed. This is improved version of recently proposed Grasshopper Optimisation Algorithm (GOA), which mimics swarming behavior of grasshoppers in the living world. To improve the performance of GOA, Opposition based learning (OBL) is introduced in Grasshopper Optimisation Algorithm. The algorithm is tested on several numerical benchmark functions and is compared with some well known optimization algorithms.

Keywords

Optimization Metaheuristics Grasshopper Optimisation Algorithm Opposition based learning Opposition based Grasshopper Optimisation Algorithm 

References

  1. 1.
    Glover, F.W., Kochenberger, G.A. (eds.): Handbook of Metaheuristics. vol. 57. Springer (2006)Google Scholar
  2. 2.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, MHS 1995, pp. 39–43, IEEE (1995)Google Scholar
  3. 3.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  5. 5.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. vol. 200, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department (2005)Google Scholar
  6. 6.
    Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)CrossRefGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic programming (1992)Google Scholar
  8. 8.
    Rechenberg, I.: Evolution strategy: nature’s way of optimization. In: Optimization: Methods and Applications, possibilities and Limitations, pp. 106–126. Springer, Heidelberg (1989)Google Scholar
  9. 9.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  12. 12.
    Glover, F.: Tabu search – Part I. ORSA J. Comput. 1(3), 190–206 (1989)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Glover, F.: Tabu search – Part II. ORSA J. Comput. 2, 4–32 (1990)CrossRefGoogle Scholar
  14. 14.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  15. 15.
    Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)CrossRefGoogle Scholar
  16. 16.
    Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)CrossRefGoogle Scholar
  17. 17.
    Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)CrossRefGoogle Scholar
  18. 18.
    Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: IEEE Congress on Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), pp. 1128–1134, IEEE (2008)Google Scholar
  19. 19.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evol. Comput. 9(2), 126–142 (2005)CrossRefGoogle Scholar
  20. 20.
    Crepinsek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)CrossRefGoogle Scholar
  21. 21.
    Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on Computational Intelligence for Modelling, Control and Automation, 2005, vol. 1, pp. 695–701. IEEE, November 2005Google Scholar
  22. 22.
    Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Global Optim. 31(4), 635–672 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)CrossRefGoogle Scholar
  24. 24.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Malaviya National Institute of Technology JaipurJaipurIndia

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