Biblio

Classification problems have been part of numerous real-life applications in fields of security, medicine, agriculture, and more. Due to the wide range of applications, there is a constant need for more accurate and efficient methods. Besides more efficient and better classification algorithms, the optimal feature set is a significant factor for better classification accuracy. In general, more features can better describe instances, but besides showing differences between instances of different classes, it can also capture many similarities that lead to wrong classification. Determining the optimal feature set can be considered a hard optimization problem for which different metaheuristics, like swarm intelligence algorithms can be used. In this paper, we propose an adaptation of hybridized swarm intelligence (SI) algorithm for feature selection problem. To test the quality of the proposed method, classification was done by k-means algorithm and it was tested on 17 benchmark datasets from the UCI repository. The results are compared to similar approaches from the literature where SI algorithms were used for feature selection, which proves the quality of the proposed hybridized SI method. The proposed method achieved better classification accuracy for 16 datasets. Higher classification accuracy was achieved while simultaneously reducing the number of used features.

With increasing demand for travelling, high-quality network service is important to people in vehicle cabins. Visible light communication (VLC) system is more appropriate than wireless local area network considering the security, communication speed, and narrow shape of the cabin. However, VLC exhibits technical limitations, such as uneven distribution of optical signals. In this regard, we propose a novel weight search bat algorithm (WSBA) to calculate a set of optimal power adjustment factors to reduce fluctuation in signal distributions. Simulation results show that the fairness of signal distribution in the cabin optimized by WSBA is better than that of the non-optimized signal distribution. Moreover, the coverage rate of WSBA is higher than that of genetic algorithm and particle swarm optimization.

For most wireless sensor networks applications it is necessary to know the locations of all sensor nodes. Since sensor nodes are usually cheap, it is impossible to equip them all with GPS devices, hence the localization process depends on few static or mobile anchor nodes with GPS devices. Range based localization methods use estimated distance between sensor and anchor nodes where the quality of estimation usually depends on the distance and angle of arrival. Localization based on such noisy data represents a hard optimization problem for which swarm intelligence algorithms have been successfully used. In this paper we propose a range based localization algorithm that uses recently developed bat algorithm. The two stage localization algorithm uses four semi-mobile anchors that are at first located at the corners of the area where sensors are deployed and after that the anchors move to their optimal positions with minimal distances to sensor nodes, but with maximal viewing angles. Our proposed algorithm is even at the first stage superior to other approaches from literature in minimizing the error between real and estimated sensor node positions and it is additionally improved at the second stage.