An Improved Bat Algorithm based on Whale Optimization Algorithm for Data Clustering

Subject Areas : Clustering and Classification

Neda Damya ¹ , Farhad Soleimanian Gharehchopogh ²

1 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
2 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN

Received: 2020-08-17 Accepted : 2020-10-16 Published : 2020-11-01

Keywords: Bat algorithm, Data clustering, Optimization, Whale Optimization Algorithm,

Abstract :

Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.

References:

1.    Soleimanian Gharehchopogh, F. and S. Haggi, An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering. Journal of Advances in Computer Engineering and Technology, 2020. 6(2): p. 79-90.
2.    Guha, R., et al., Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection. Applied Soft Computing, 2020. 93: p. 106341.
3.    Gharehchopogh, F.S., I. Maleki, and S.R. Khaze, A new optimization method for dynamic travelling salesman problem with hybrid ant colony optimization algorithm and particle swarm optimization. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2013. 2(2): p. 352-358.
4.    Gharehchopogh, F.S. and H. Gholizadeh, A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 2019. 48: p. 1-24.
5.    Xiao, Y., et al., Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering. Pattern Recognition, 2019. 90: p. 183-195.
6.    Sun, L., et al., Combining density peaks clustering and gravitational search method to enhance data clustering. Engineering Applications of Artificial Intelligence, 2019. 85: p. 865-873.
7.    Rabani, H. and F. Soleimanian Gharehchopogh, An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks. Journal of Advances in Computer Research, 2019. 10(3): p. 13-30.
8.    Rahnema, N. and F.S. Gharehchopogh, An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimedia Tools and Applications, 2020: p. 1-26.
9.    Huang, X., et al., DSKmeans: A new kmeans-type approach to discriminative subspace clustering. Knowledge-Based Systems, 2014. 70: p. 293-300.
10.    Yang, X.-S., A New Metaheuristic Bat-Inspired Algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J.R. González, et al., Editors. 2010, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 65-74.
11.    Mirjalili, S. and A. Lewis, The Whale Optimization Algorithm. Advances in Engineering Software, 2016. 95: p. 51-67.
12.    Osmani, A., J.B. Mohasefi, and F.S. Gharehchopogh, Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm. The Computer Journal, 2020.
13.    Shayanfar, H. and F.S. Gharehchopogh, Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 2018. 71: p. 728-746.
14.    Gharehchopogh, F.S. and S.K. Mousavi, A New Feature Selection in Email Spam Detection by Particle Swarm Optimization and Fruit Fly Optimization Algorithms. Journal of Computer and Knowledge Engineering, 2019. 2(2).
15.    Gharehchopogh, F.S., H. Shayanfar, and H. Gholizadeh, A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 2019: p. 1-48.
16.    Gharehchopogh, F.S., S.R. Khaze, and I. Maleki, A new approach in bloggers classification with hybrid of k-nearest neighbor and artificial neural network algorithms. 2015.
17.    Abedi, M. and F.S. Gharehchopogh, An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems. Intelligent Data Analysis, 2020. 24: p. 309-338.
18.    Gandomi, A.H. and X.-S. Yang, Chaotic bat algorithm. Journal of Computational Science, 2014. 5(2): p. 224-232.
19.    Lin, J.-H., et al. A Chaotic Levy Flight Bat Algorithm for Parameter Estimation in Nonlinear Dynamic Biological Systems. in CIT 2012. 2012.
20.    Sabba, S. and S. Chikhi, A discrete binary version of bat algorithm for multidimensional knapsack problem. Int. J. Bio-Inspired Comput., 2014. 6(2): p. 140–152.
21.    Zhou, Y., et al., A Hybrid Bat Algorithm with Path Relinking for the Capacitated Vehicle Routing Problem, in Metaheuristics and Optimization in Civil Engineering, X.-S. Yang, G. Bekdaş, and S.M. Nigdeli, Editors. 2016, Springer International Publishing: Cham. p. 255-276.
22.    Cai, X., X.-z. Gao, and Y. Xue, Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspired Comput., 2016. 8(4): p. 205–214.
23.    Zhu, B., et al., A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization. Computational Intelligence and Neuroscience, 2016. 2016: p. 6097484.
24.    Yammani, C., S. Maheswarapu, and S.K. Matam, A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models. International Journal of Electrical Power & Energy Systems, 2016. 79: p. 120-131.
25.    Nakamura, R.Y.M., et al. BBA: A Binary Bat Algorithm for Feature Selection. in 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images. 2012.
26.    Mirjalili, S., S.M. Mirjalili, and X.-S. Yang, Binary bat algorithm. Neural Computing and Applications, 2014. 25(3): p. 663-681.
27.    Yilmaz, S., E.U. Kucuksille, and Y. Cengiz, Modified bat algorithm. ELEKTRONIKA IR ELEKTROTECHNIKA, 2014. 20(2): p. 71-78.
28.    Li, L. and Y. Zhou, A novel complex-valued bat algorithm. Neural Computing and Applications, 2014. 25(6): p. 1369-1381.
29.    Mallikarjuna, B., K. Reddy, and O. Hemakesaavulu, Economic load dispatch problem with valve-point effect using a binary bat algorithm. ACEEE International Journal of Elecrtical and Power Engineering, 2013. 4(3): p. 33-38.
30.    Xiaodong, W., J. ZHANG, and H. XUE, K-Means Clustering Algorithm Based on Bat Algorithm. Journal of Jilin University (Information Science Edition), 2016. 6.
31.    Sood, M. and S. Bansal, K-Medoids Clustering Technique using Bat Algorithm. International Journal of Applied Information Systems, 2013. 5: p. 20-22.
32.    Nguyen, T.-T., et al. Hybrid Bat Algorithm with Artificial Bee Colony. in Intelligent Data analysis and its Applications, Volume II. 2014. Cham: Springer International Publishing.
33.    Murugan, R., et al., Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch. Applied Soft Computing, 2018. 72: p. 189-217.
34.    Luo, J., F. He, and J. Yong, An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intelligent Data Analysis, 2020. 24: p. 581-606.
35.    Safara, F., et al., An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network. IEEE Access, 2020. 8: p. 48428-48437.
36.    Zhu, L.F., et al., Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators. IEEE Access, 2020. 8: p. 80536-80560.
37.    Calixto, V. and G. Celani. A literature review for space planning optimization using an evolutionary algorithm approach: 1992-2014. 2015.
38.    Lim, S.M. and K.Y. Leong, A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems. In Nature-inspired Methods for Stochastic Robust and Dynamic Optimization, 2018.
39.    website1, https://archive.ics.uci.edu/ml/index.php. 2020.