Authors : Kwame Antwi Boasiako

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5dtwa8xj

Scribd : https://tinyurl.com/3jtt63nn

DOI : https://doi.org/10.38124/ijisrt/26apr2221

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : An improved algorithm that is used to alleviate the downsides of the whale optimization algorithm's (WOA) drawbacks which are slow convergence rate and falling into local optima, is proposed. There are five main variables introduced into this enhanced algorithm. These include the Cauchy density function, quasi-opposition-based learning, Euclidean distance, cubic chaotic mapping, and weight addition. The proposed algorithm was compared to the original WOA, two recent variants, and one other swarm-based intelligent algorithm, on twenty-three benchmark functions. The parameters used for comparison were Optimal value, Mean, Standard Deviation, and convergence rate. The improved WOA yields better optimal values with minimal iterations. The results validate that the Improved WOA outperforms the other four algorithms.

Keywords : Whale Optimization Algorithm (WOA); Metaheuristic Optimization; Swarm Intelligence; Cubic Chaotic Mapping; Cauchy Density Function; Quasi-Opposition-Based Learning (QOBL); Euclidean Distance; Population Diversity; Convergence Rate; Global Optimization.

References :

1. M. O. Okwu and L. K. Tartibu, Metaheuristic Optimization : Nature-Inspired Algorithms Swarm and Computational Intelligence , Theory and Applications. 2021.
2. P. D. Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Weinsier, Modern Music-Inspired Optimization Algorithms for Electric Power Systems Modeling, Analysis and Practice. 2019.
3. J. A. Parejo, A. Ruiz-Cortés, S. Lozano, and P. Fernandez, “Metaheuristic optimization frameworks: A survey and benchmarking,” Soft comput, vol. 16, no. 3, pp. 527–561, 2012, doi: 10.1007/s00500-011-0754-8.
4. I. Boussaïd, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,” Inf Sci (N Y), vol. 237, no. February, pp. 82–117, 2013, doi: 10.1016/j.ins.2013.02.041.
5. H. Zhang, J. Li, M. Hong, and Y. Man, “Artificial intelligence algorithm-based multi-objective optimization model of flexible flow shop smart scheduling,” Applications of Artificial Intelligence in Process Systems Engineering, pp. 447–472, Jan. 2021, doi: 10.1016/B978-0-12-821092-5.00008-5.
6. S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
7. G. Chen, X. Zhao, Y. Xiang, X. Zeng, and H. Long, “Research on Multi-objective Active Power Optimization Simulation of Novel Improved Whale Optimization Algorithm,” IAENG International Journal of Applied Mathematics, vol. 51, no. 3, pp. 1–20, 2021.
8. W. Yankai, W. Shilong, L. Dong, S. Chunfeng, and Y. Bo, “An improved multi-objective whale optimization algorithm for the hybrid flow shop scheduling problem considering device dynamic reconfiguration processes,” Expert Syst Appl, vol. 174, no. April 2020, p. 114793, 2021, doi: 10.1016/j.eswa.2021.114793.
9. M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, L. Abualigah, M. A. Elaziz, and D. Oliva, “Ewoa-opf: Effective whale optimization algorithm to solve optimal power flow problem,” Electronics (Switzerland), vol. 10, no. 23, 2021, doi: 10.3390/electronics10232975.
10. S. Chakraborty, A. K. Saha, R. Chakraborty, and M. Saha, “An enhanced whale optimization algorithm for large scale optimization problems,” Knowl Based Syst, vol. 233, p. 107543, 2021, doi: 10.1016/j.knosys.2021.107543.
11. X. Liang, S. Xu, Y. Liu, and L. Sun, “A Modified Whale Optimization Algorithm and Its Application in Seismic Inversion Problem,” vol. 2022, 2022.
12. H. Mohammed and T. Rashid, “A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design,” Neural Comput Appl, vol. 32, no. 18, pp. 14701–14718, 2020, doi: 10.1007/s00521-020-04823-9.
13. Y. Ling, Y. Zhou, and Q. Luo, “Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization,” IEEE Access, vol. 5, pp. 6168–6186, 2017, doi: 10.1109/ACCESS.2017.2695498.
14. H. Chen, Y. Xu, M. Wang, and X. Zhao, “A balanced whale optimization algorithm for constrained engineering design problems,” Appl Math Model, vol. 71, pp. 45–59, 2019, doi: 10.1016/j.apm.2019.02.004.
15. Y. Sun, T. Yang, and Z. Liu, “A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems,” Applied Soft Computing Journal, vol. 85, p. 105744, 2019, doi: 10.1016/j.asoc.2019.105744.
16. H. Chen, C. Yang, A. A. Heidari, and X. Zhao, “An efficient double adaptive random spare reinforced whale optimization algorithm,” Expert Syst Appl, vol. 154, p. 113018, 2020, doi: 10.1016/j.eswa.2019.113018.
17. I. N. Trivedi, P. Jangir, A. Kumar, N. Jangir, and R. Totlani, “A novel hybrid PSO–WOA algorithm for global numerical functions optimization,” Advances in Intelligent Systems and Computing, vol. 554, pp. 53–60, 2018, doi: 10.1007/978-981-10-3773-3_6.
18. W. Z. Sun, J. S. Wang, and X. Wei, “An improved whale optimization algorithm based on different searching paths and perceptual disturbance,” Symmetry (Basel), vol. 10, no. 6, pp. 1–31, 2018, doi: 10.3390/sym10060210.
19. P. Nandagopal, “Hybrid WOA-BAT Optimization for Performance Enhancement of Coexisting WBANs,” 2021.
20. M. Vallabhaneni, B. Maddula, S. Jayan, and R. Subramani, “Chaotic Hooke-Jeeves Algorithm using Cubic map with MATLAB code,” 2020 IEEE International Conference for Innovation in Technology, INOCON 2020, pp. 2020–2023, 2020, doi: 10.1109/INOCON50539.2020.9298244.
21. H. Chen, M. Wang, and X. Zhao, “A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems,” Appl Math Comput, vol. 369, p. 124872, Mar. 2020, doi: 10.1016/J.AMC.2019.124872.
22. B. Mandal and P. K. Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization,” International Journal of Electrical Power & Energy Systems, vol. 53, no. 1, pp. 123–134, Dec. 2013, doi: 10.1016/J.IJEPES.2013.04.011.
23. M. O. Okwu and L. K. Tartibu, “Grey Wolf Optimizer,” Studies in Computational Intelligence, vol. 927, pp. 43–52, 2021, doi: 10.1007/978-3-030-61111-8_5.