Abstract: 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 users 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.
R. Padmapriya and D. Maheswari, 2017. Channel Allocation Optimization using African Buffalo Optimization-Super Vector Machine for Networks. Asian Journal of Information Technology, 16: 783-788.