Improved Whale Optimization Algorithm for Numerical Optimization

  • A. K. Vamsi KrishnaEmail author
  • Tushar Tyagi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1086)


In this paper, an Improved Whale Optimization Algorithm which is intended towards the better optimization of the solutions under the category of meta-heuristic algorithms is proposed. Falling under the genre of nature-inspired algorithms, the Improved Whale Optimization delivers better results with comparatively better convergence techniques used. A detailed study and comparative analysis have been made between the principal and the modified algorithms, and a variety of fitness functions has been used to confirm the efficiency of the improved algorithm over the older version. The merits with nature-inspired algorithms include distributed computing, reusable components, network processes, mutations and crossovers leading to better results, randomness and stochasticity.


Optimization algorithms Nature-inspired algorithms Whale Optimization Algorithm Bubble-net forging Meta-heuristic algorithm Randomization Fitness function Spiral updating Encircling mechanism 


  1. 1.
    X.S. Yang, Nature-Inspired Metaheuristic Algorithms (Luniver Press, 2010)Google Scholar
  2. 2.
    X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Springer, Berlin, Heidelberg, 2010), pp. 65–74Google Scholar
  3. 3.
    S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)Google Scholar
  4. 4.
    M.M. Mafarja, S. Mirjalili, Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing (2017)Google Scholar
  5. 5.
    P.D.P. Reddy, V.V. Reddy, T.G. Manohar, Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew. Wind Water Sol. 4(1), 3 (2017)Google Scholar
  6. 6.
    A.N. Jadhav, N. Gomathi, WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alexandria Eng. J. (2017)Google Scholar
  7. 7.
    A. Kaveh, Sizing optimization of skeletal structures using the enhanced whale optimization algorithm. in Applications of metaheuristic optimization algorithms in civil engineering (Springer, 2017), pp. 47–69Google Scholar
  8. 8.
    T. Liao et al., Ant colony optimization for mixed-variable optimization problems. IEEE Trans. Evol. Comput. 18(4), 503–518 (2014)CrossRefGoogle Scholar
  9. 9.
    X.-S. Yang, S. Deb, Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)zbMATHGoogle Scholar
  10. 10.
    I. Aljarah, H. Faris, S. Mirjalili, Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 1–15 (2016)Google Scholar
  11. 11.
    M.-Y. Cheng, D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)CrossRefGoogle Scholar
  12. 12.
    G. Kaur, S. Arora, Chaotic whale optimization algorithm. J. Comput. Des. Eng. 5, 275–284 (2018)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.School of Electronics and Electrical EngineeringLovely Professional UniversityJalandharIndia

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