GACN: Self-Clustering Genetic Algorithm for Constrained Networks
نویسندگان
چکیده
منابع مشابه
An Adaptive LEACH-based Clustering Algorithm for Wireless Sensor Networks
LEACH is the most popular clastering algorithm in Wireless Sensor Networks (WSNs). However, it has two main drawbacks, including random selection of cluster heads, and direct communication of cluster heads with the sink. This paper aims to introduce a new centralized cluster-based routing protocol named LEACH-AEC (LEACH with Adaptive Energy Consumption), which guarantees to generate balanced cl...
متن کاملSelf-Adaptive Genetic Algorithm for Clustering
Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the al...
متن کاملRepeated Record Ordering for Constrained Size Clustering
One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology, and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggreg...
متن کاملSelf-Stabilizing Clustering Algorithm for Ad hoc Networks
Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such network cannot rely on centralized and organized connectivity. The clustering problem consists in partitioning network nodes into groups called clusters, thus giving at the network a hierarchical organization. Clustering is commonly used in order to limit the amount of routi...
متن کاملA fast algorithm for robust constrained clustering
The application of “concentration” steps is the main principle behind Forgy’s kmeans algorithm and Rousseeuw and van Driessen’s fast-MCD algorithm. Although they share this principle, it is not completely straightforward to combine both algorithms for developing a clustering method which is not affected by a certain proportion of outlying observations and that is able to cope with non spherical...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Communications Letters
سال: 2017
ISSN: 1089-7798
DOI: 10.1109/lcomm.2016.2641420