TY  - JOUR
PB  - Blue Eyes Intelligence Engineering and Sciences Publication
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063694591&partnerID=40&md5=4d101b113a98cd21aded1f52e9f6ccd4
ID  - scholars12210
SN  - 22783075
TI  - Parameters tuning of hybrid artificial bee colony search based strategy for t-way testing
Y1  - 2019///
A1  - Alazzawi, A.K.
A1  - Rais, H.M.
A1  - Basri, S.
N1  - cited By 9
IS  - 5s
SP  - 204
N2  - 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. © 2019, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved.
JF  - International Journal of Innovative Technology and Exploring Engineering
VL  - 8
AV  - none
EP  - 212
ER  -