Abstract
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.
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References
X.S. Yang, Nature-Inspired Metaheuristic Algorithms (Luniver Press, 2010)
X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Springer, Berlin, Heidelberg, 2010), pp. 65–74
S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
M.M. Mafarja, S. Mirjalili, Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing (2017)
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)
A.N. Jadhav, N. Gomathi, WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alexandria Eng. J. (2017)
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. 47–69
T. Liao et al., Ant colony optimization for mixed-variable optimization problems. IEEE Trans. Evol. Comput. 18(4), 503–518 (2014)
X.-S. Yang, S. Deb, Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)
I. Aljarah, H. Faris, S. Mirjalili, Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 1–15 (2016)
M.-Y. Cheng, D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)
G. Kaur, S. Arora, Chaotic whale optimization algorithm. J. Comput. Des. Eng. 5, 275–284 (2018)
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Vamsi Krishna, A.K., Tyagi, T. (2021). Improved Whale Optimization Algorithm for Numerical Optimization. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_6
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DOI: https://doi.org/10.1007/978-981-15-1275-9_6
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