Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rechenberg, I.: Cybernetic solution path of an experimental problem, Royal Aircraft Establishment Library Translation, No. 1122, Aug 1965

    Google Scholar 

  2. Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    MATH  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  5. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  6. Glover, F.: Heuristic for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. Xue, X., Yao, M., Cheng, R.: A Novel Selection Operator of Cultural Algorithm. Knowl. Eng. Manag. 123, 71–77 (2012)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Cai, Y., Wang, J.: Differential evolution with hybrid linkage crossover. Inf. Sci. 320, 244–287 (2015)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Amity University, Noida, India

    Abhishek Dixit & Sushil Kumar

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  3. Rajiv Gandhi Institute of Petroleum Technology, Noida, India

    Rohit Bansal

Authors
  1. Abhishek Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sushil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Millie Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Dixit .

Editor information

Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dixit, A., Kumar, S., Pant, M., Bansal, R. (2019). CA-DE: Hybrid Algorithm Based on Cultural Algorithm and DE. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_16

Download citation

Publish with us

Policies and ethics

Search

Navigation