Adaptive Exploration Artificial Bee Colony on Evolutionary Clustering

Doaa  Alsamee; Hazim  Albedran; Reza  Ramezani

doi:10.30572/2018/KJE/170205

Authors

Doaa Alsamee Faculty of Computer Science and Mathematics, Department of Computer Science, University of Kufa, Iraq
Hazim Albedran Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq
Reza Ramezani Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

DOI:

https://doi.org/10.30572/2018/KJE/170205

Keywords:

Evolutionary Clustering, Optimization Algorithms, Metaheuristics, Swarm Intelligence, Unsupervised Learning

Abstract

The current study was an attempt to examine the new application of the Adaptive Exploration Artificial Bee Colony algorithm in evolutionary clustering. An optimized version of the traditional Artificial Bee Colony (ABC) algorithm, AEABC relies heavily on a probability parameter that is based on distance. In this regard, a balanced relationship of exploration and exploitation leads to the dynamic parameter adaption, resulting in the spatial distribution of solutions. As a result, AEABC provides an efficient performance for solving dynamic clustering tasks, involving consistent data changes and constant reformation of cluster structures. To conduct the present study, the performance of AEABC on evolutionary clustering was compared with that of other metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results confirmed that AEABC produced lower performance error rates than competing algorithms in all three scenarios representing best case, worst case and average case performance. This established both its reliability as well as robust efficiency. AEABC indicated convergence performance because the algorithm initiates with quick development in the first stages then reached stable points effectively, which shows its ability to position itself well in changing data environments. Moreover, the findings could confirm AEABC as an effective means for evolutionary clustering, which makes it a suitable candidate for applications involving dynamic systems, such as social network analysis, customer segmentation, and anomaly detection. By applying AEABC to evolutionary clustering, this paper contributes to expanding the range of applications for swarm intelligence algorithms in handling real-world, dynamic data challenges

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