A Novel Approach for Optimal Rule Creation and Classification in Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architecture (original) (raw)

2015

Abstract

An Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architecture (ASAALI) is a self-learning network management system [1] in which the collection and analysis of data from all Autonomous Nodes (AN), for generation of rule-sets was a significantly important but time consuming process. As a solution for efficient analysis and creation of optimized rule-sets an Ant Colony Optimization (ACO) based classifier AntMiner-CC [2] is used based on its performance comparison with other well-known learning based classifiers. Rule-sets are later used by Adaptation and Planning Network layer of ASAALI for imposing decisions over the heterogeneous network environment. Keywords: ASAALI, autonomous network management, Ant-Miner-CC, ACO, learning, classification.

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