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An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

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Abstract

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

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References

  1. Abdel-Basset M, Manogaran G, el-Shahat D, Mirjalili S (2018) A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Futur Gener Comput Syst 85:129–145

    Article  Google Scholar 

  2. Abedi M, Gharehchopogh FS (2020) An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems. Intelligent Data Analysis 24(2):309–338

    Article  Google Scholar 

  3. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer

  4. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput & Applic, 1–21

  5. Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust Comput, 1–19

  6. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. International Journal of Computer Science, Engineering and Applications 5(1):19

    Article  Google Scholar 

  7. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  8. Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  9. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  10. Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125

    Article  Google Scholar 

  11. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science 25:456–466

    Article  Google Scholar 

  12. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  13. Banharnsakun A (2017) A MapReduce-based artificial bee colony for large-scale data clustering. Pattern Recogn Lett 93:78–84

    Article  Google Scholar 

  14. Boushaki SI, Kamel N, Bendjeghaba O (2018) A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl 96:358–372

    Article  Google Scholar 

  15. Cui L, Zhang K, Li G, Fu X, Wen Z, Lu N, Lu J (2018) Modified Gbest-guided artificial bee colony algorithm with new probability model. Soft Comput 22(7):2217–2243

    Article  Google Scholar 

  16. Das P, Das DK, Dey S (2018) A modified bee Colony optimization (MBCO) and it's hybridization with k-means for an application to data clustering. Appl Soft Comput 70:590–603

    Article  Google Scholar 

  17. Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci 293:125–145

    Article  Google Scholar 

  18. Du Z, Han D, Li K-C (2019) Improving the performance of feature selection and data clustering with novel global search and elite-guided artificial bee colony algorithm. J Supercomput:1–38

  19. Farshidpour S, Keynia F (2012) Using artificial bee colony Algorithm for MLP Training on software defect prediction. Orient J Comp Sci Technol. 5(2)

  20. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  21. Ghany KKA et al. (2020) A hybrid modified step whale optimization algorithm with Tabu search for data clustering. J King Saud Univ-Comp Info Sci

  22. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm and Evolutionary Computation 48:1–24

    Article  Google Scholar 

  23. Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 1–48

  24. Guo C et al. (2019) Data clustering using the cooperative search based artificial bee Colony algorithm. In International Conference on Intelligent Computing. Springer

  25. İnkaya T, Kayalıgil S, Özdemirel NE (2015) Ant colony optimization based clustering methodology. Appl Soft Comput 28:301–311

    Article  Google Scholar 

  26. Jadhav AN, Gomathi N (2017) WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alexandria Eng J

  27. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666

    Article  Google Scholar 

  28. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer ….

  29. Long W, Liang X, Cai S, Jiao J, Zhang W (2018) An improved artificial bee colony with modified augmented Lagrangian for constrained optimization. Soft Comput 22(14):4789–4810

    Article  Google Scholar 

  30. Luqman M et al. (2017) Radial artificial bee colony algorithm for constraint engineering problems. Pakistan Journal of Science. 69(1)

  31. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  32. Mao M, Duan Q, Zhang L (2017) Artificial bee Colony algorithm based on adaptive search equation and extended memory. Cybern Syst 48(5):459–482

    Article  Google Scholar 

  33. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(9):1455–1465

    Article  Google Scholar 

  34. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  35. Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Article  Google Scholar 

  36. Van der Merwe D, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In evolutionary computation, 2003. CEC'03. The 2003 Congress on. IEEE

  37. Wang J, du P, Niu T, Yang W (2017) A novel hybrid system based on a new proposed algorithm—multi-objective whale optimization algorithm for wind speed forecasting. Appl Energy 208:344–360

    Article  Google Scholar 

  38. Wang G-G, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput & Applic 31(7):1995–2014

    Article  Google Scholar 

  39. Wu X, Wu B, Sun J, Qiu S, Li X (2015) A hybrid fuzzy K-harmonic means clustering algorithm. Appl Math Model 39(12):3398–3409

    Article  Google Scholar 

  40. Wu Z-X, Huang K-W, Girsang AS (2018) A whole crow search algorithm for solving data clustering. In 2018 conference on technologies and applications of artificial intelligence (TAAI). IEEE

  41. Xiang W-L et al (2017) A grey artificial bee colony algorithm. Appl Soft Comput 60:1–17

    Article  Google Scholar 

  42. Xue Y et al. (2018) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 1–18.

  43. Yan X, Zhu Y, Zou W, Wang L (2012) A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing 97:241–250

    Article  Google Scholar 

  44. Zabihi F, Nasiri B (2018) A novel history-driven artificial bee colony algorithm for data clustering. Appl Soft Comput 71:226–241

    Article  Google Scholar 

  45. Zhong F, Li H, Zhong S (2017) An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. Eng Appl Artif Intell 58:134–156

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia branch, Islamic Azad University, Urmia, Iran

    Nouria Rahnema & Farhad Soleimanian Gharehchopogh

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  1. Nouria Rahnema
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  2. Farhad Soleimanian Gharehchopogh
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Rahnema, N., Gharehchopogh, F.S. An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimed Tools Appl 79, 32169–32194 (2020). https://doi.org/10.1007/s11042-020-09639-2

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  • DOI: https://doi.org/10.1007/s11042-020-09639-2

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