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Repulsion-Based Grey Wolf Optimizer

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Proceedings of International Conference on Artificial Intelligence and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1164))

Abstract

In this paper, a novel algorithm called Repulsion-based Grey Wolf Optimizer (R-GWO) is presented. The proposed algorithm is an improvement of the already existing popular swarm intelligence algorithm, called Grey Wolf Optimizer (GWO). The aim of the proposed work is to enhance the balance between exploitation and exploration in GWO by introducing a repulsion factor. This repulsion factor drifts the population away from the non-promising regions and brings it closer to the optimal solution. To compare the performance of R-GWO and GWO, both the algorithms are run on 16 benchmark fitness functions. The results indicate that R-GWO performs better as compared to the conventional GWO in terms of convergence speed and in discovering the global optimal solution.

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References

  1. R. Eberhart, J. Kennedy, Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  2. E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. 2232–48 (2009)

    Google Scholar 

  3. B.C. Mohan, R. Baskaran, A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 4618–27 (2012)

    Google Scholar 

  4. S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 46–61 (2014)

    Google Scholar 

  5. X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (Springer, Berlin, 2010). 65–74

    Google Scholar 

  6. S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 163–91 (2017)

    Google Scholar 

  7. S. Yu, Z. Wu, H. Wang, Z. Chen, A hybrid particle swarm optimization algorithm based on space transformation search and a modified velocity model, in High Performance Computing and Applications (Springer, Berlin, 2010), pp. 522–527.

    Google Scholar 

  8. X. Yu, J. Cao, H. Shan, L. Zhu, J. Guo, An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization. Sci. World J. (2014)

    Google Scholar 

  9. A. Zhu, C. Xu, Z. Li, J. Wu, Z. Liu, Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. J. Syst. Eng. Electr. 317–28 (2015)

    Google Scholar 

  10. E. Emary, H.M. Zawbaa, A.E. Hassanien, Binary grey wolf optimization approaches for feature selection. Neurocomputing 371–81 (2016)

    Google Scholar 

  11. U.K. Chakraborty, Genetic and evolutionary computing. Inf. Sci. (Ny) 178, 4419–4420 (2008)

    Article  Google Scholar 

  12. S. Zhang, Q. Luo, Y. Zhou, Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method. Int. J. Comput. Intell. Appl. (2017)

    Google Scholar 

  13. N. Singh, S.B. Singh, A novel hybrid GWO-SCA approach for optimization problems. Eng. Sci. Technol. 1586–601 (2017)

    Google Scholar 

  14. N. Mittal, U. Singh, B.S. Sohi, Modified grey wolf optimizer for global engineering optimization. Appl. Comput. Intell. Soft Comput. (2016)

    Google Scholar 

  15. H. Mittal, M. Saraswat, An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evol. Comput. 15–32 (2019)

    Google Scholar 

  16. H. Shah-Hosseini, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio. Inspired comput. (2009)

    Google Scholar 

  17. P. Agarwal, S. Mehta, Empirical analysis of five nature-inspired algorithms on real parameter optimization problems. Artif. Intell. Rev. 383–439 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science Engineering and Information Technology, Jaypee Institute of Information Technology, Sector 62, Noida, UP, India

    Ankita Wadhwa & Manish Kumar Thakur

Authors
  1. Ankita Wadhwa
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  2. Manish Kumar Thakur
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Corresponding author

Correspondence to Ankita Wadhwa .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Poonam Bansal

  2. Department of Electrical and Electronics Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Meena Tushir

  3. Department of Automation and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Rajeev Srivastava

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wadhwa, A., Thakur, M.K. (2021). Repulsion-Based Grey Wolf Optimizer. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_36

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