Greedy heuristic algorithm for solving series of eee components classification problems
URI (for links/citations):http://iopscience.iop.org/article/10.1088/1757-899X/122/1/012011
Kazakovtsev, L. A.
Antamoshkin, A. N.
Fedosov, V. V.
Институт управления бизнес-процессами и экономики
Кафедра экономики и информационных технологий менеджмента
Journal Name:IOP Conference Series: Materials Science and Engineering
Journal Quartile in Scopus:без квартиля
Journal Quartile in Web of Science:без квартиля
Bibliographic Citation:Kazakovtsev, L. A. Greedy heuristic algorithm for solving series of eee components classification problems [Текст] / L. A. Kazakovtsev, A. N. Antamoshkin, V. V. Fedosov // IOP Conference Series: Materials Science and Engineering. — 2016. — Т. 122 (№ 1).
lgorithms based on using the agglomerative greedy heuristics demonstrate precise and stable results for clustering problems based on k- means and p-median models. Such algorithms are successfully implemented in the processes of production of specialized EEE components for using in space systems which include testing each EEE device and detection of homogeneous production batches of the EEE components based on results of the tests using p-median models. In this paper, authors propose a new version of the genetic algorithm with the greedy agglomerative heuristic which allows solving series of problems. Such algorithm is useful for solving the k-means and p-median clustering problems when the number of clusters is unknown. Computational experiments on real data show that the preciseness of the result decreases insignificantly in comparison with the initial genetic algorithm for solving a single problem.