A Novel Chaotic Northern Bald Ibis Optimization Algorithm for Solving Different Cluster Problems [ICCICC18 #155]

Abstract

This article proposes a new optimal data clustering method for finding optimal clusters of data by incorporating chaotic maps into the standard NOA. NOA, a newly developed optimization technique, has been shown to be efficient in generating optimal results with lowest solution cost. The incorporation of chaotic maps into metaheuristics enables algorithms to diversify the solution space into two phases: explore and exploit more. To make the NOA more efficient and avoid premature convergence, chaotic maps are incorporated in this work, termed as CNOAs. Ten different chaotic maps are incorporated individually into standard NOA for testing the optimization performance. The CNOA is first benchmarked on 23 standard functions. Secondly, testing was done on the numerical complexity of the new clustering method which utilizes CNOA, by solving 10 UCI data cluster problems and 4 web document cluster problems. The comparisons have been made with the help of obtaining statistical and graphical results. The superiority of the proposed optimal clustering algorithm is evident from the simulations and comparisons.