Krill herd (KH) is a stochastic nature-inspired optimization algorithm, it has been successfully used to solve many involved optimization problems. Occasionally, poor exploration (diversification) capability affects the performance of krill herd algorithm (KHA). In this paper, we proposed a new hybridization strategy, namely, hybrid the krill herd algorithm with the harmony search (HS) algorithm (Harmony-KHA), to improve the data clustering technique. This hybridization strategy seeks to enhance the global (diversification) search capability of the KH algorithm to obtain the best global optima. The proposed algorithm are conducted through the addition of the global search operator from the HS algorithm in order to improve the exploration around the optimal solution in KH and thus kill individuals move towards the global best solution. The proposed algorithm is applied to keep the best krill individuals during the updating positions of the krill individuals. Experiments were conducted using four standard datasets from the UCI Machine Learning Repository, which is commonly used in the domain of data clustering. The results showed that the proposed hybrid the KH algorithm with the HS algorithm (Harmony-KHA) is produced very accurate clusters especially in the large dataset. Furthermore, the Harmony-KHA obtained a high convergence rate and it can overcome the other comparative algorithms. The proposed algorithm is compared with other well-known based on data clustering algorithms including the original KH algorithm.
HuTSungSY. Consensus clustering. Intelligent Data Analysis2005; 9(6): 551-565.
2.
AbualigahLMKhaderATHanandehES. A novel weighting scheme applied to improve the text document clustering techniques. Innovative Computing, Optimization and Its Applications, Springer2018; 305-320.
3.
SaidaIBNadjetKOmarB. A new algorithm for data clustering based on cuckoo search optimization. Genetic and Evolutionary Computing, Springer2014; 55-64.
4.
GuoLWangGGGandomiAHAlaviAHDuanH. A new improved krill herd algorithm for global numerical optimization. Neurocomputing2014; 138: 392-402.
5.
AmiriEMahmoudiS. Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Applied Soft Computing2016; 41: 15-21.
HatamlouA. Black hole: A new heuristic optimization approach for data clustering. Information Sciences2013; 222: 175-184.
8.
NandaSJPandaG. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary Computation2014; 16: 1-18.
9.
AbualigahLMKhaderATAl-BetarMAAlomariOA. Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Systems with Applications2017; 84: 24-36.
10.
ChenNXuZSXiaMM. Hierarchical hesitant fuzzy K-means clustering algorithm. Applied Mathematics â A Journal of Chinese Universities2014; 29(1): 1-17.
AbualigahLMKhaderATAl-BetarMA. Unsupervised feature selection technique based on harmony search algorithm for improving the text clustering. 7th International Conference on Computer Science and Information Technology (CSIT)2016; 1-6.
13.
AbualigahLMKhaderATAl-BetarMA. Multi-objectives-based text clustering technique using K-mean algorithm. Computer Science and Information Technology (CSIT), 7th International Conference on, IEEE2016; 1-6.
AbualigahLMKhaderAT. Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. The Journal of Supercomputing2017; 1-23.
16.
MizoojiKHaghighatAForsatiR. Data clustering using bee colony optimization. 7th International Multi-Conference on Computing in the Global IT2012; 189-194.
17.
AbualigahLMKhaderATHanandehESGandomiAH. A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Applied Soft Computing2017.
18.
YangFSunTZhangC. An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Systems with Applications2009; 36(6): 9847-9852.
19.
AbualigahLMKhaderATAl-BetarMAAwadallahMA. A krill herd algorithm for efficient text documents clustering. Computer Applications & Industrial Electronics (ISCAIE), IEEE Symposium on, IEEE2016; 67-72.
20.
AbualigahLMQHanandehES. Applying genetic algorithms to information retrieval using vector space model. International Journal of Computer Science, Engineering and Applications2015; 5(1): 19.
21.
AbualigahLMKhaderATAl-BetarMA. Unsupervised feature selection technique based on genetic algorithm for improving the text clustering. Computer Science and Information Technology (CSIT), 7th International Conference on, IEEE2016; 1-6.
YanXZhuYZouWWangL. A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing2012; 97: 241-250.
24.
ShahrakiHZahiriSH. Ant colony optimization and decision function estimation. Intelligent Decision Technologies; (Preprint):1-8.
25.
YaghiniMGhazanfariN. Tabu-KM: A hybrid clustering algorithm based on tabu search approach. International Journal of Industrial Engineering2010; 21(2).
26.
Mohd AliaOAl-BetarMAMandavaRKhaderAT. Data clustering using harmony search algorithm. International Conference on Swarm, Evolutionary, and Memetic Computing, Springer2011; 79-88.
27.
GandomiAHAlaviAH. Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation2012; 17(12): 4831-4845.
28.
WangGGDebSGandomiAHAlaviAH. Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing2016; 177: 147-157.
29.
WangGGGandomiAHAlaviAHDebS. A multi-stage krill herd algorithm for global numerical optimization. International Journal on Artificial Intelligence Tools2016; 25(02): 1550030.
30.
GeemZWKimJHLoganathanG. A new heuristic optimization algorithm: harmony search. Simulation2001; 76(2): 60-68.
31.
AssadADeepK. Applications of harmony search algorithm in data mining: A survey. Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Springer2016; 863-874.
32.
WangLLiLP. An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. International Journal of Electrical Power & Energy Systems2013; 44(1): 832-843.
33.
BolajiALKhaderATAl-BetarMAAwadallahMA. A hybrid nature-inspired artificial bee colony algorithm for uncapacitated examination timetabling problems. Journal of Intelligent Systems2015; 24(1): 37-54.
34.
JensiRJijiGW. MBA-LF: A new data clustering method using modified bat algorithm and levy flight. ICTACT Journal on Soft Computing2015; 6(1).
35.
LiuXFuH. An effective clustering algorithm with ant colony. JCP2010; 5(4): 598-605.
36.
KaoYTZaharaEKaoIW. A hybridized approach to data clustering. Expert Systems with Applications2008; 34(3): 1754-1762.
37.
AbualigahLMKhaderATHanandehES. A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science2017.
38.
JensiRJijiGW. An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Applied Soft Computing2016; 46: 230-245.
39.
XuRWunschD. Survey of clustering algorithms. IEEE Transactions on Neural Networks2005; 16(3): 645-678.
40.
CelebiMEKingraviHAVelaPA. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications2013; 40(1): 200-210.
41.
AbualigahLMKhaderATAlBetarMAHanandehES. A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering. Management2017.
42.
WangGGuoLWangHDuanHLiuLLiJ. Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Computing and Applications2014; 24(3-4): 853-871.
43.
WangGGHossein GandomiAHossein AlaviA. A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes2013; 42(6): 962-978.
44.
WangGGGandomiAHAlaviAHHaoGS. Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Computing and Applications2014; 25(2): 297-308.
45.
BolajiALAl-BetarMAAwadallahMAKhaderATAbualigahLM. A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing2016; 49: 437-446.
46.
AskarzadehA. Solving electrical power system problems by harmony search: A review. Artificial Intelligence Review2017; 47(2): 217-251.
47.
KuoRLinL. Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems2010; 49(4): 451-462.
48.
Dos Santos CoelhoLMarianiVC. An improved harmony search algorithm for power economic load dispatch. Energy Conversion and Management2009; 50(10): 2522-2526.
49.
WahidJHussinNM. Hybrid harmony search with great deluge for UUM CAS curriculum based course timetabling. Journal of Telecommunication, Electronic and Computer Engineering (JTEC)2017; 9(1-2): 33-38.
