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Internet of Things (IoT) in Smart Systems and Applications
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General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
,
etc
.
Acceptance Rate:
12%
APC:
1000 USD
Average Days to Accept:
87 days
Journal Metrics:
Impact Factor 2023: 0.9
4.2
2023
CiteScore
57th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2025-02-10
All the 141 papers published in JAIT in 2024 have been indexed by Scopus.
2025-01-23
JAIT Vol. 16, No. 1 has been published online!
2024-06-07
JAIT received the CiteScore 2023 with 4.2, ranked #169/394 in Category Computer Science: Information Systems, #174/395 in Category Computer Science: Computer Networks and Communications, #226/350 in Category Computer Science: Computer Science Applications
Home
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Published Issues
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2022
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Volume 13, No. 3, June 2022
>
JAIT 2022 Vol.13(3): 259-264
doi: 10.12720/jait.13.3.259-264
The Local Branching as a Learning Strategy in the Evolutionary Algorithm: The Case of the Set-Union Knapsack Problem
Isma Dahmani
1
, Meriem Ferroum
1
, and Mhand Hifi
2
1. University of Sciences and Technology Houari Boumedienne, Algeria
2. University of Picardy Jules Verne, France
Abstract
—In this paper, we introduce the local branching as a learning strategy for approximately solving the set-union knapsack problem; that is an NP-hard combination optimization problem. The designed method is based upon three features: (i) applying a swarm optimization for generating a set of current particles, (ii) using an iterative search for providing a series of diversified solutions linking some particles of the population and, (iii) injecting a local branching as a learning strategy for enhancing the global best solution: it can be viewed as a driving strategy employed for guiding particles towards the best position. The performance of the method is evaluated on benchmark instances of the literature, where its provided bounds are compared to those reached by the best methods available in the literature. New bounds have been discovered.
Index Terms
—evolutionary, knapsack, learning, branching
Cite: Isma Dahmani, Meriem Ferroum, and Mhand Hifi, "The Local Branching as a Learning Strategy in the Evolutionary Algorithm: The Case of the Set-Union Knapsack Problem," Journal of Advances in Information Technology, Vol. 13, No. 3, pp. 259-264, June 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
7-C026-Algeria+France
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