Greedy heuristic algorithm for solving series of eee components classification problems
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URI (для ссылок/цитирований):
http://iopscience.iop.org/article/10.1088/1757-899X/122/1/012011https://elib.sfu-kras.ru/handle/2311/69878
Автор:
Kazakovtsev, L. A.
Antamoshkin, A. N.
Fedosov, V. V.
Коллективный автор:
Институт управления бизнес-процессами и экономики
Кафедра экономики и информационных технологий менеджмента
Дата:
2016-10Журнал:
IOP Conference Series: Materials Science and EngineeringКвартиль журнала в Scopus:
без квартиляКвартиль журнала в Web of Science:
без квартиляБиблиографическое описание:
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).Аннотация:
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.