TY  - JOUR
T1  - Channel Allocation Optimization using African Buffalo Optimization-Super Vector Machine for Networks
AU - Padmapriya, R. AU - Maheswari, D. 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 10
SP  - 783
EP  - 788
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.783.788
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.783.788
KW  - Networks
KW  -African buffalo optimization
KW  -Genetic algorithm
KW  -super vector machine
KW  -utilization
KW  -communication
AB  - Recent technologies utilize the communication network effectively with respect to limited spectrum
allocation. In order to perform proper communication, it is necessary to utilize the resource with minimum aspect
since, if number of network user’s increases then the interference also slightly increased. To avoid interference
and to use minimum resource it is necessary to create a new hybrid channel allocation scheme and optimize the
performance metrics. The proposed hybrid model applies the Super Vector Machine (SVM) classification to
African Buffalo Optimization (ABO), it is compared in terms of "survival of the fittest" with Genetic Algorithm
(GA) based SVM. Normally, SVM is a classification technique used to classify the nonlinear data and ABO is
also an effective problem solving mechanism. Hence, the combination of ABO and SVM provides best fitness
function in communication network by focusing on minimizing the interference in web traffic. The performance
metrics considered for evaluation are energy, accuracy and processor utilization. Finally, the experimental
results were shows that the proposed ABO-SVM method is best when compared with the GA-SVM.
ER  -