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In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy...
This work introduces a cluster head selection optimization model in wireless sensor networks (WSN). It applies the grey wolf optimization. The optimization of WSN cluster heads greatly influences the network life time. Grey wolf optimization(GWO) is a recently proposed optimizer that has a variety of successful applications. Therefore, adapted and applied in here to solve the CH selection problem...
In this work, a feature selection algorithm based on moth-flame optimization (MFO) is proposed. Moth-flame optimization (MFO) is a recently proposed swarm intelligent optimization algorithm that mimics the motion of moths. The proposed algorithm is applied in the domain of machine learning for feature selection to find the optimal feature combination using wrapper-based feature selection mode. In...
This research expands the scope of wireless sensor network (WSN) optimization from single objective to multi objective optimization. It introduces a WSN's energy-aware and coverage preserve hierarchal clustering and routing model based on multi-objective bat swarm optimization algorithm. Two objectives are taken into consideration; coverage and nodes residual energies. The proposed model optimizes...
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