Clustering Using an Improved Krill Herd Algorithm
نویسندگان
چکیده
منابع مشابه
Clustering Using an Improved Krill Herd Algorithm
In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some we...
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The hybrid approach is proposed to show that the clusters also show the swarm behavior. Krill herd algorithm is used to show the simulation of the herding behavior of the krill individuals. Density based approach is used for discovering the clusters and to show the region with sufficiently high density into clusters of krill individuals that of the arbitrary shape in environment. The minimum di...
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Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...
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Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several tes...
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ژورنال
عنوان ژورنال: Algorithms
سال: 2017
ISSN: 1999-4893
DOI: 10.3390/a10020056