A novel hybridization strategy for krill herd algorithm applied to clustering techniques

• Published in: Applied soft computing Vol. 60; pp. 423 - 435
• Main Authors: Abualigah, Laith Mohammad, Khader, Ahamad Tajudin, Hanandeh, Essam Said, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2017
• Subjects: Data clustering; Global exploration; Hybridization; Krill herd algorithm; Text clustering; Text clustering; Hybridization; Global exploration; Krill herd algorithm; Data clustering
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.06.059

•A novel hybrid of krill herd algorithm with harmony search algorithm.•The enhancement includes adding the operator of the harmony search algorithm to the krill herd algorithm.•Anew probability value (Def) is proposed to control the harmony search operator to explore the search space effectively.•Investigate the proposed algorithm for the text and data clustering problems. Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.