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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...
We concentrate on the single car, full information elevator problem. Here ldquofull informationrdquo means that the arrival time, the origins and destinations of passengers are all assumed known beforehand. The importance of studying full information problem lies in that we can know the value of the future information and evaluate the existing scheduling methods for the elevator system. We aim to...
The computational complexity of a texture classification algorithm is limited by the dimensionality of the feature space. A feature selection algorithm that can reduce the dimensionality of problem is often desirable, which has been studied by many authors because of its impact on the complexity of classifiers, Furthermore, feature selection in high dimension space is a NP hard problem. This paper...
The world of ants is a reach source of inspiration since real ants are able to solve collectively relatively complex problems. Particularly, several ant based clustering algorithms have been proposed in the literature. These clustering models were derived from several phenomena among real ants such as cemetery organization, recognition system, building alive structures, etc. In this work, we try to...
The problem of automation of feature selection problem solving is solved. The bacteria foraging optimization is considered. The method of feature selection based on the bacteria foraging optimization is proposed.
We present a novel optimization algorithm that is inspired by the foraging behavior of a colony of honey bees in this paper. The new optimization method is tested on a few well-known bench mark functions. This paper demonstrates the capability of convergence of the new algorithm to global optima. Further, the results obtained by the proposed method are compared with Ant Colony Optimization (ACO) approach.
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