Modification of coot optimization algorithm (COA) with adaptive sigmoid increasing inertia weight for global optimization

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

In this paper, the classical coot optimization algorithm (COA) is modified to improve its overall performance in the exploration phase by adding an adaptive sigmoid inertia weight-based method. The modified coot optimization algorithm (mCOA) was successfully assessed using 13 standard benchmark test functions, which are frequently used to evaluate metaheuristic optimization algorithms. The MATLAB software was utilized to conduct simulation tests, and the outcome was compared with the performance of the original COA, the particle swarm optimization, and the genetic algorithm reported in the literature. The findings showed that the proposed algorithm outperformed the other algorithms on ten (10) of the 13 benchmark functions, while it maintained a competitive performance on the remaining three benchmark test functions. This indicates that mCOA provides a significant improvement to the original COA, thus making it suitable for resolving optimization problems in diverse fields. As a result, the proposed algorithm is recommended for adoption to solve real-life engineering optimization problems.

Keywords

coot optimization algorithm / modification / algorithm / adaptive weight

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] V. Soni, A. Sharma, V. Singh, A critical review on nature inspired optimization algorithms, IOP Conf. Ser.: Mater. Sci. Eng., 1099 (2021), 012055. https://doi.org/10.1088/1757-899x/1099/1/012055

[2] A. F. Seini Yussif, T. Seini, Improved F-parameter mountain gazelle optimizer (IFMGO): a comparative analysis on engineering design problems, IRJET, 10 (2023), 810-816.

[3] N. Khodadadi, E. S. M. El-Kenawy, F. De Caso, A. H. Alharbi, D. S. Khafaga, A. Nanni, The mountain gazelle optimizer for truss structures optimization, Appl. Comput. Intell., 3 (2023), 116-144. https://doi.org/10.3934/aci.2023007

[4] R. Rani, S. Jain, H. Garg, A review of nature-inspired algorithms on single-objective optimization problems from 2019 to 2023, Artif. Intell. Rev., 57 (2024), 126. https://doi.org/10.1007/s10462-024-10747-w

[5] P. Agarwal, S. Mehta, Nature-inspired algorithms: state-of-art, problems and prospects, International Journal of Computer Applications, 100 (2014), 14-21. https://doi.org/10.5120/17593-8331

[6] I. Naruei, F. Keynia, A new optimization method based on COOT bird natural life model, Expert Syst. Appl., 183 (2021), 115352. https://doi.org/10.1016/j.eswa.2021.115352

[7] R. R. Mostafa, A. G. Hussien, M. A. Khan, S. Kadry, F. A. Hashim, Enhanced COOT optimization algorithm for dimensionality reduction, Proceedings of Fifth International Conference of Women in Data Science at Prince Sultan University, 2022, 43-48. https://doi.org/10.1109/WiDS-PSU54548.2022.00020

[8] P. K. Mandal, A review of classical methods and nature-inspired algorithms (NIAs) for optimization problems, Results in Control and Optimization, 13 (2023), 100315. https://doi.org/10.1016/j.rico.2023.100315

[9] M. Jain, V. Saihjpal, N. Singh, S. B. Singh, An overview of variants and advancements of PSO algorithm, Appl. Sci., 12 (2022), 8392. https://doi.org/10.3390/app12178392

[10] A. F. Seini Yussif, E. Twumasi, E. A. Frimpong, Modified mountain gazelle optimizer based on logistic chaotic mapping and truncation selection, IRJET, 10 (2023), 1769-1776.

[11] A. F. Seini Yussif, E. Twumasi, E. A. Frimpong, Performance enhancement of elephant herding optimization algorithm using modified update operators, Jurnal Nasional Teknik Elektro, 2 (2023), 109-118. https://doi.org/10.25077/jnte.v12n2.1124.2023

[12] N. K. Prah II, E. A. Frimpong, E. Twumasi, Modified individual experience mayfly algorithm, Carpathian Journal of Electrical Engineering, 16 (2022), 62-74.

[13] A. Bright, A. K. Emmanuel, T. Elvis, F. A. Emmanuel, Enhanced adaptive simulated based artificial gorilla troop optimizer for global optimisation, IJEEAS, 6 (2023), 63-76.

[14] M. Aslan, İ. Koç, Modified coot bird optimization algorithm for solving community detection problem in social networks, Neural Comput. Appl., 36 (2024), 5595-5619. https://doi.org/10.1007/s00521-024-09567-4

[15] E. H. Houssein, F. A. Hashim, S. Ferahtia, H. Rezk, Battery parameter identification strategy based on modified coot optimization algorithm, J. Energy Storage, 46 (2022), 103848. https://doi.org/10.1016/j.est.2021.103848

[16] Z. Chen, Y. Wang, T. H. T. Chan, X. Li, S. Zhao, A particle swarm optimization algorithm with sigmoid increasing inertia weight for structural damage identification, Appl. Sci., 12 (2022), 3429. https://doi.org/10.3390/app12073429

[17] T. Seini, A. F. S. Yussif, I. M. Katali, Enhancing mountain gazelle optimizer (MGO) with an improved F-parameter for global optimization, IRJET, 10 (2023), 921-930.

[18] N. Moniz, H. Monteiro, No free lunch in imbalanced learning, Knowl.-Based Syst., 227 (2021), 107222. https://doi.org/10.1016/j.knosys.2021.107222