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The capability of solving the simultaneous localization and mapping (SLAM) problem is one of the fundamental tasks of mobile robots and many research has focused on this problem over the last decades. In this paper, the SLAM problem is considered as the problem of finding an optimal path through a tree resulting in minimum costs. For this purpose, we apply the Ant Colony Optimization meta-heuristic,...
Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields. When there is a need to learn the inherent grouping structure of data in an unsupervised manner, ant-based clustering stand out as the most widely used group of swarm-based clustering algorithms. Under this perspective, this paper presents a new...
A swarm-intelligence-inspired approach to clustering data is described. The algorithm consists of two stages. In the first stage of the algorithm, ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated using a reformulated fuzzy C-means (FCM) criterion. In the second stage, the best cluster centers found are used as the initial cluster centers for the FCM...
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