A hybrid discrete Artificial Bee Colony - GRASP algorithm for clustering

Abstract

This paper presents a new hybrid algorithm, which is based on the concepts of the Artificial Bee Colony (ABC) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines an Artificial Bee Colony Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. As the feature selection problem is a discrete problem, a modification of the initially proposed Artificial Bee Colony optimization algorithm, a Discrete Artificial Bee Colony optimization algorithm, is proposed in this study. The performance of the algorithm is compared with other popular metaheuristic methods like classic genetic algorithms, tabu search, GRASP, ant colony optimization, particle swarm optimization and honey bees mating optimization algorithm. In order to assess the efficacy of the proposed algorithm, this methodology is evaluated on datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the corrected clustered samples is very high and is larger than 98%.