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Soft Computing for Problem Solving 2019 pp 15–30Cite as

Performance Analysis of Whale Optimization Algorithm Based on Strategy Parameter

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1138))

Abstract

The performance of heuristic algorithms highly depends on the parameter values of the algorithms. Whale optimization algorithm (WOA) is a newly developed heuristic algorithm which has strategy parameter a that decreases linearly from 2 to 0 as iteration increases. In this paper, two algorithms, modified whale optimization algorithm-1 (MWOA-1) and modified whale optimization algorithm-2 (MWOA-2), have been proposed based on the variation of the strategy parameter a. The experiments are performed on a set of 23 benchmark problems. Results are compared with original WOA, gravitational search algorithm and grasshopper optimization algorithm. Based on the analysis of results, it is concluded that the overall performance of MWOA-1 and MWOA-2 is better than others on scalable unimodal function with dim = 30, scalable multimodal functions with dim = 30 and low-dimensional multimodal functions.

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References

  1. J.H. Holland, Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)

    Article  Google Scholar 

  2. D. Simon, Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  3. A. Singh, K. Deep, Real coded genetic algorithm operators embedded in gravitational search algorithm for continuous optimization. Int. J. Intell. Syst. Appl. 7(12), 1–22 (2015)

    Google Scholar 

  4. A. Singh, K. Deep, Novel hybridized variants of gravitational search algorithm for constraint optimization. Int. J. Swarm Intell. 3(1), 1–22 (2017)

    Article  MathSciNet  Google Scholar 

  5. A. Singh, K. Deep, Hybridized gravitational search algorithms with real coded genetic algorithms for integer and mixed integer optimization problems, in Proceedings of Sixth International Conference on Soft Computing for Problem Solving (Springer, Singapore, 2017), pp. 84–112

    Google Scholar 

  6. J. Kennedy, Particle swarm optimization, in Encyclopedia of Machine Learning (Springer, US, 2011), pp. 760–766

    Google Scholar 

  7. M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  8. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. Evolut. Comput. IEEE Trans. 1(1), 67–82 (1997)

    Article  Google Scholar 

  9. S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  10. A. Kaveh, M.I. Ghazaan, in Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mech. Based Des. Struct. Mach. 1–18 (2016)

    Google Scholar 

  11. S.K. Cherukuri, S.R. Rayapudi, A novel global MPP tracking of photovoltaic system based on whale optimization algorithm. Int. J. Renew. Energy Develop. 5(3), 225–232 (2016)

    Article  Google Scholar 

  12. I. Aljarah, H. Faris, S. Mirjalili, in Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 1–15 (2016)

    Google Scholar 

  13. R.H. Bhesdadiya, S.A. Parmar, I.N. Trivedi, P. Jangir, M. Bhoye, N. Jangir, Optimal active and reactive power dispatch problem solution using whale optimization algorithm. Indian J. Sci. Technol. 9(S1), 1–6 (2016), https://doi.org/10.17485/ijst/2016/v9i(s1)/101941

  14. Z. Yan, J. Sha, B. Liu, W. Tian, J. Lu, An ameliorative whale optimization algorithm for multi-objective optimal allocation of water resources in Handan, China. Water 10(1), 87–116 (2018)

    Article  Google Scholar 

  15. J. Nasiri, F.M. Khiyabani, A whale optimization algorithm (WOA) approach for clustering. Cogent Math. Statist. 5(1), 1–13 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  16. G. Kaur, S. Arora, Chaotic whale optimization algorithm. J. Comput. Des. Eng. 5(3), 275–284 (2018)

    Google Scholar 

  17. A.N. Jadhav, N. Gomathi, WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex. Eng. J. 57(3), 1569–1584 (2018)

    Article  Google Scholar 

  18. M.A. Elaziz, D. Oliva, Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers. Manag. 171, 1843–1859 (2018)

    Article  Google Scholar 

  19. S. Mirjalili, S.M. Mirjalili, S. Saremi, S. Mirjalili, Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters, in Nature-Inspired Optimizers (Springer, Cham, 2020), pp. 219–238

    Google Scholar 

  20. A. Singh, Laplacian whale optimization algorithm. J. Int. J. Syst. Assur. Eng. Manag. 10(4), 713–730 (2019)

    Google Scholar 

  21. F.J. Lobo, C.F. Lima, Z. Michalewicz, in Parameter Setting in Evolutionary Algorithms, vol. 54 (Springer Science & Business Media, 2007), pp. 19–33

    Google Scholar 

  22. S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Janki Devi Memorial College, Sir Ganga Ram Hospital Marg, New Delhi, Delhi, 110060, India

    Amarjeet Singh

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

    Kusum Deep

Authors
  1. Amarjeet Singh
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  2. Kusum Deep
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Corresponding author

Correspondence to Amarjeet Singh .

Editor information

Editors and Affiliations

  1. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics, National Institute of Technology Silchar, Silchar, India

    Kedar Nath Das

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Singh, A., Deep, K. (2020). Performance Analysis of Whale Optimization Algorithm Based on Strategy Parameter. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-3290-0_2

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