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Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

Authors:

Seid Miad Zandavi,

Ali AnaissiAuthors Info & Claims

ACM/IMS Transactions on Data Science, Volume 2, Issue 2

Article No.: 14, Pages 1 - 13

https://doi.org/10.1145/3437260

Published: 08 April 2021 Publication History

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Abstract

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

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Cited By

Suleiman BAnaissi AXiao YYaqub WRaju AAlyassine W(2023)Supervised Learning-Based Indoor Positioning System Using WiFi FingerprintsProceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)10.1007/978-3-031-33743-7_5(56-71)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33743-7_5

Index Terms

Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm
1. Applied computing
  1. Education
    1. Distance learning
2. Mathematics of computing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM/IMS Transactions on Data Science

ACM/IMS Transactions on Data Science Volume 2, Issue 2

May 2021

149 pages

ISSN:2691-1922

DOI:10.1145/3454114

Editor:
Beng Chin Ooi
National University of Singapore, Singapore

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Copyright © 2021 Association for Computing Machinery.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2021

Accepted: 01 November 2020

Revised: 01 August 2020

Received: 01 September 2019

Published in TDS Volume 2, Issue 2

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Cited By

Suleiman BAnaissi AXiao YYaqub WRaju AAlyassine W(2023)Supervised Learning-Based Indoor Positioning System Using WiFi FingerprintsProceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)10.1007/978-3-031-33743-7_5(56-71)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33743-7_5

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