Parameters Tuning of Hybrid Artificial Bee Colony Search based Strategy for t-way Testing
Ammar K Alazzawi1, Helmi Md Rais2, Shuib Basri3
1Ammar K Alazzawi, Department of Computer and Information Sciences, University Technology, Bandar Seri Iskandar, Perak, Malaysia.
2Helmi Md Rais, Department of Computer and Information Sciences, University Technology, Bandar Seri Iskandar, Perak, Malaysia.
3Shuib Basri, Department of Computer and Information Sciences, University Technology, Bandar Seri Iskandar, Perak, Malaysia.
Manuscript received on 03 February 2019 | Revised Manuscript received on 10 February 2019 | Manuscript Published on 22 March 2019 | PP: 204-212 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3418018319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Hybrid Artificial Bee Colony (HABC) Strategy is latterly developed based on hybridize of an artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. In order to ensure that HABC could perform for t-way testing as useful as other strategies to generate best performance, there are a number of parameters of an HABC algorithm such as the number of colony size (NBees), the number of food source, limit, the number of cycles (maxCycle), weight factor (w) and learning factors (C1, C2) that required to be tuned. In this paper, the process of parameters tuning for hybrid artificial bee colony algorithm has been shown as well as t-way testing, where has been adopted a standard covering array CA (N, 2, 57). The obtained experiment results illustrate that HABC strategy can generate the most minimum and sufficiently results compared to other strategies.
Keywords: In this Paper, the Process of Parameters Tuning for Hybrid Artificial bee Colony Algorithm has been Shown.
Scope of the Article: Computer Science and Its Applications