Advertisement

CA-DE: Hybrid Algorithm Based on Cultural Algorithm and DE

  • Abhishek DixitEmail author
  • Sushil Kumar
  • Millie Pant
  • Rohit Bansal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Optimization problems can be articulated by numerous practical problems. These glitches stance a test for the academics in the proposal of proficient procedures skilled in digging out the preeminent elucidation with the slightest computing cost. In this study, we worked on differential evolution and cultural algorithm, conglomerates the features of both the algorithms, and proposes a new evolutionary algorithm. This jointure monitors the complex collaboration amalgam of two evolutionary algorithms, where both are carried out in analogous. The novel procedure termed as CA-DE accomplishes an inclusive inhabitant that is pooled among both metaheuristics algorithms concurrently. The aspect of the recycled approval action in credence space is to update the information of the finest individuals with the present information. This collective collaboration arises among both the algorithms and is presented to mend the eminence of resolutions, ahead of the individual performance of both the algorithms. We have applied the newly proposed algorithm on a set of six standard benchmark optimization problems to evaluate the performance. The comparative results presented demonstrate that CA-DE has an encouraging accomplishment and expandable conducts while equated with new contemporary advanced algorithms.

Keywords

Nature-inspired computation (NIC) Cultural algorithm (CA) Evolutionary computation (EC) Differential evolution (DE) 

References

  1. 1.
    Rechenberg, I.: Cybernetic solution path of an experimental problem, Royal Aircraft Establishment Library Translation, No. 1122, Aug 1965Google Scholar
  2. 2.
    Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)zbMATHGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Storn, R., Price, K.V.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Glover, F.: Heuristic for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)CrossRefGoogle Scholar
  7. 7.
    Peng, B., Reynolds, R.G.: Cultural algorithms: knowledge learning in dynamic environments. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1751–1758 (2004)Google Scholar
  8. 8.
    Kim, Y., Cho, S.-B.: A hybrid cultural algorithm with local search for traveling salesman problem. IEEE International Conference on Robotics and Automation (CIRA), pp. 188–192 (2009)Google Scholar
  9. 9.
    Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf. Sci. (2016)Google Scholar
  10. 10.
    Alia, M.Z., Awadc,N.H., Suganthanc, P.N., Reynolds, R.G., Lin, C.-J., Chen, C.-H., Lin, C.-T.: A modified cultural algorithm with a balanced performance for the differential evolution frameworks. Sci. Direct Knowl.-Based Syst. 73–86 (2016)Google Scholar
  11. 11.
    Sun, Y., Zhang, L., Gu, X.: A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems. Neurocomputing 98, 76–89 (2012)CrossRefGoogle Scholar
  12. 12.
    Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Annual Conference on Evolutionary Programming, pp. 131–139 (1994)Google Scholar
  13. 13.
    Xue, X., Yao, M., Cheng, R.: A Novel Selection Operator of Cultural Algorithm. Knowl. Eng. Manag. 123, 71–77 (2012)CrossRefGoogle Scholar
  14. 14.
    He, J., Xu, F.: Chaotic-search-based cultural algorithm for solving unconstrained optimization problem. Model. Simul. Eng. 2011, 1–6 (2011)Google Scholar
  15. 15.
    Guo, Y.-N., Cheng, J., Cao, Y.-Y., Lin, Y.: A novel multi-population cultural algorithm adopting knowledge migration. Soft. Comput. 15, 897–905 (2011)CrossRefGoogle Scholar
  16. 16.
    Oleiwi, B.K., Roth, H., Kazem, B.I.: A hybrid approach based on ACO and GA for multi objective mobile robot path planning. Appl. Mech. Mater. 527, 203–212 (2014)CrossRefGoogle Scholar
  17. 17.
    Cai, Y., Wang, J.: Differential evolution with hybrid linkage crossover. Inf. Sci. 320, 244–287 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Mahi, M., Baykan, Ö.K., Kodaz, H.: A new hybrid method based on particle swarm optimization, antcolony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. 30, 484–490 (2015)CrossRefGoogle Scholar
  19. 19.
    Das, P.K., Behera, H.S., Panigrahi, B.K.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evolut. Comput. (2016)Google Scholar
  20. 20.
    Nguyen, T.T., Yao, X.: An experimental study of hybridizing cultural algorithms and local search. Int. J. Neural Syst. 18, 1–18 (2008)Google Scholar
  21. 21.
    Zheng, Y.-J.: A hybrid neuro-fuzzy network based on differential biogeography-based optimization for online population classification in earthquakes. Ling, H.-F., Chen, S.-Y., Xue, J.-Y.: IEEE Trans. Fuzzy Syst. 23(4) (2015)Google Scholar
  22. 22.
    Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39, 680–691 (2009)CrossRefGoogle Scholar
  23. 23.
    Zhang, J., Avasarala, V., Sanderson, A.C., Mullen, T.: Differential evolution for discrete optimization: AN EXPERIMENTAL Study on combinatorial auction problems. In: Proceedings of the IEEE World Congress on Computational Intelligence, Hong Kong, China, pp. 2794–2800 (2008)Google Scholar
  24. 24.
    Qin, K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1785–1791 (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhishek Dixit
    • 1
    Email author
  • Sushil Kumar
    • 1
  • Millie Pant
    • 2
  • Rohit Bansal
    • 3
  1. 1.Amity UniversityNoidaIndia
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.Rajiv Gandhi Institute of Petroleum TechnologyNoidaIndia

Personalised recommendations