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Annals of Data Science

17-04-2024

An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering

Author: Farhad Soleimanian Gharehchopogh

Published in: Annals of Data Science

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Abstract

Data clustering is one of the main issues in the optimization problem. It is the process of clustering a group of items into several groups. Items within each group have the greatest similarity and the least similarity to things in other groups. It is employed in various domains and applications, including biology, business, and consumer analysis, document clustering, web, banking, and image processing, to name a few. In this paper, two new methods are proposed using hybridization of the Bald Eagle Search (BES) Algorithm with the African Vultures Optimization Algorithm (AVOA) (BESAVOA) and BESAVOA with Opposition Based Learning (BESAVOA-OBL) for data clustering. AVOA is used to find the centers of the clusters and improve the centrality of the groups obtained by the BES algorithm. Primary vectors are created based on the population of eagles, and then each vector is used BESAVOA to search the centers of the clusters. The proposed methods (BESAVOA and BESAVOA-OBL) are evaluated on 16 UCI datasets, based on the number of generations, number of iterations, execution time, and convergence. The results show that the BESAVOA-OBL fits better than the other algorithms. The results show that compared to other algorithms, BESAVOA-OBL is more effective by a ratio of 12.42 percent.

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Gharehchopogh FS, Khargoush AA (2023) A chaotic-based interactive autodidactic school algorithm for data clustering problems and its application on COVID-19 disease detection. Symmetry 15(4):1–26. https://doi.org/10.3390/sym15040894CrossRef

Wang L, Wang H, Han X, Zhou W (2021) A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm. Comput Commun 174(1):205–214. https://doi.org/10.1016/j.comcom.2021.03.021CrossRef

Piri J, Mohapatra P, Acharya B, Gharehchopogh FS, Gerogiannis VC, Kanavos A, Manika S (2022) Feature selection using artificial gorilla troop optimization for biomedical data: a case analysis with COVID-19 data. Mathematics 10(15):1–31. https://doi.org/10.3390/math10152742CrossRef

Shi Y (2022) Big data and big data analytics. In: Advances in big data analytics: theory, algorithms and practices. Springer, Singapore, pp 3–21. https://doi.org/10.1007/978-981-16-3607-3_1

Olson DL (2007) Introduction to business data mining. Irwin Professional Publishing, Burr Ridge

Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178. https://doi.org/10.1007/s40745-017-0112-5CrossRef

Yong S, Ying-jie T, Gang K, Yi P, Jianping L (2011) Optimization based data mining: theory and applications. Berlin. https://doi.org/10.1007/978-0-85729-504-0CrossRef

Nadimi-Shahraki MH, Fatahi A, Zamani H, Mirjalili S (2022) Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data. Mathematics 10(15):2770. https://doi.org/10.3390/math10152770CrossRef

Fasihy H, Shahraki MHN (2018) Incremental mining maximal frequent patterns from univariate uncertain data. Knowl Based Syst 152:40–50. https://doi.org/10.1016/j.knosys.2018.04.001CrossRef

10.

Özbay E, Özbay FA (2023) Interpretable pap-smear image retrieval for cervical cancer detection with rotation invariance mask generation deep hashing. Comput Biol Med 154(1):106574. https://doi.org/10.1016/j.compbiomed.2023.106574CrossRef

11.

Gharehchopogh FS, Nadimi-Shahraki MH, Barshandeh S, Abdollahzadeh B, Zamani H (2023) Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. J Bionic Eng 20(1):158–183. https://doi.org/10.1007/s42235-022-00255-4CrossRef

12.

Gharehchopogh FS, Shayanfar H (2022) Automatic data clustering using farmland fertility metaheuristic algorithm. In: Advances in swarm intelligence: variations and adaptations for optimization problems. Springer, Berlin, pp 199–215. https://doi.org/10.1007/978-3-031-09835-2_11

13.

Nadimi-Shahraki MH, Asghari Varzaneh Z, Zamani H, Mirjalili S (2022) Binary starling murmuration optimizer algorithm to select effective features from medical data. Appl Sci 13(1):564. https://doi.org/10.3390/app13010564CrossRef

14.

Alsattar HA, Zaidan A, Zaidan B (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53(1):2237–2264. https://doi.org/10.1007/s10462-019-09732-5CrossRef

15.

Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158(1):107408. https://doi.org/10.1016/j.cie.2021.107408CrossRef

16.

Özbay FA, Özbay E (2023) A new approach for gender detection from voice data: feature selection with optimization methods. J Fac Eng Archit Gazi Univ 38(2):1179–1192

17.

Özbay FA (2023) A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems. Eng Sci Technol Int J 41(1):101408. https://doi.org/10.1016/j.jestch.2023.101408CrossRef

18.

Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36(10):5887–5958. https://doi.org/10.1002/int.22535CrossRef

19.

Hashemi SE, Tavana M, Bakhshi M (2022) A new particle swarm optimization algorithm for optimizing big data clustering. SN Comput Sci 3(4):311. https://doi.org/10.1007/s42979-022-01208-8CrossRef

20.

Qtaish A, Braik M, Albashish D, Alshammari MT, Alreshidi A, Alreshidi EJ (2024) Optimization of K-means clustering method using hybrid capuchin search algorithm. J Supercomput 80(2):1728–1787. https://doi.org/10.1007/s11227-023-05540-5CrossRef

21.

Xiao Y, H-b Li, Zhang Y-p (2024) DBGSA: a novel data adaptive bregman clustering algorithm. Eng Appl Artif Intell 131(1):107846. https://doi.org/10.1016/j.engappai.2024.107846CrossRef

22.

Haeri Boroujeni SP, Pashaei E (2023) A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems. Iran J Comput Sci 7(1):65–101. https://doi.org/10.1007/s42044-023-00160-xCrossRef

23.

Li Y, Chu X, Tian D, Feng J, Mu W (2021) Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm. Appl Soft Comput 113(2):107924. https://doi.org/10.1016/j.asoc.2021.107924CrossRef

24.

Edwin Dhas P, Sankara Gomathi B (2020) A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data. J Supercomput 76(8):5679–5693. https://doi.org/10.1007/s11227-019-02953-zCrossRef

25.

Zhang X, Lin Q, Mao W, Liu S, Dou Z, Liu G (2021) Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization. Appl Soft Comput 101(1):1–23. https://doi.org/10.1016/j.asoc.2020.107061CrossRef

26.

Kaur A, Kumar Y (2021) A new metaheuristic algorithm based on water wave optimization for data clustering. Evol Intel 15(1):759–783. https://doi.org/10.1007/s12065-020-00562-xCrossRef

27.

Gupta Y, Saini A (2019) A new swarm-based efficient data clustering approach using KHM and fuzzy logic. Soft Comput 23(1):145–162. https://doi.org/10.1007/s00500-018-3514-1CrossRef

28.

Alswaitti M, Albughdadi M, Isa NAM (2018) Density-based particle swarm optimization algorithm for data clustering. Expert Syst Appl 91(1):170–186. https://doi.org/10.1016/j.eswa.2017.08.050CrossRef

29.

Zhao R, Wang Y, Xiao G, Liu C, Hu P, Li H (2021) A selfish herd optimization algorithm based on the simplex method for clustering analysis. J Supercomput 77(8):8840–8910. https://doi.org/10.1007/s11227-020-03597-0CrossRef

30.

Pacifico LDS, Ludermir TB (2021) An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering. Nat Comput 20(3):611–636. https://doi.org/10.1007/s11047-020-09809-zCrossRef

31.

Jensi R, Jiji GW (2016) An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Appl Soft Comput 46(1):230–245. https://doi.org/10.1016/j.asoc.2016.04.026CrossRef

32.

Di Martino F, Sessa S (2021) A Novel quantum inspired genetic algorithm to initialize cluster centers in fuzzy c-means. Expert Syst Appl 191(1):116340. https://doi.org/10.1016/j.eswa.2021.116340CrossRef

33.

Zhou Y, Wu H, Luo Q, Abdel-Baset M (2019) Automatic data clustering using nature-inspired symbiotic organism search algorithm. Knowl Based Syst 163(1):546–557. https://doi.org/10.1016/j.knosys.2018.09.013CrossRef

34.

Su Z-g, Wang P-h, Shen J, Li Y-g, Y-f Z, Hu E-j (2012) Automatic fuzzy partitioning approach using Variable string length Artificial Bee Colony (VABC) algorithm. Appl Soft Comput 12(11):3421–3441. https://doi.org/10.1016/j.asoc.2012.06.019CrossRef

35.

Alswaitti M, Albughdadi M, Mat Isa NA (2019) Variance-based differential evolution algorithm with an optional crossover for data clustering. Appl Soft Comput 80(5):1–17. https://doi.org/10.1016/j.asoc.2019.03.013CrossRef

36.

Amiri E, Mahmoudi S (2016) Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Appl Soft Comput 41(1):15–21. https://doi.org/10.1016/j.asoc.2015.12.008CrossRef

37.

Niknam T, Olamaei J, Amiri B (2008) A hybrid evolutionary algorithm based on ACO and SA for cluster analysis. J Appl Sci 8(1):2695–2702. https://doi.org/10.3923/jas.2008.2695.2702CrossRef

38.

Kao Y-T, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762. https://doi.org/10.1016/j.eswa.2007.01.028CrossRef

39.

Yang X-S (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Berlin, Heidelberg, pp 240–249. https://doi.org/10.1007/978-3-642-32894-7_27

40.

Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169(1):1–12. https://doi.org/10.1016/j.compstruc.2016.03.001CrossRef

41.

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97(2):849–872. https://doi.org/10.1016/j.future.2019.02.028CrossRef

42.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95 - international conference on neural networks. 1942–1948, vol 4. https://doi.org/10.1109/ICNN.1995.488968

43.

Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114(1):163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002CrossRef

44.

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95(2):51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

45.

Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53(3):2237–2264. https://doi.org/10.1007/s10462-019-09732-5CrossRef

Title: An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering
Author: Farhad Soleimanian Gharehchopogh
Publication date: 17-04-2024
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00525-4

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