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A Multi-objective Evolutionary Algorithms Approach to Optimize a Task Scheduling Problem

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Applied Informatics (ICAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1277))

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Abstract

Nowadays, the size of the problems to be solved in the business world has increased largely; since companies have more resources and more demand for products and services from customers. As a result, different meta-heuristics have been developed in the computing world with the aim of finding an optimal solution in a shorter runtime. Involving a real-life case, this paper will present the approach of a multi-objective task scheduling model, solved with evolutionary algorithms; specifically, NSGA-II and SPEA2. In addition, a mathematical model was proposed and its solution was calculated in order to obtain results that allow us to compare the accuracy of the results obtained by the proposed algorithms. The running time and total cost of the task scheduling were the metrics for the evaluation of the results. Between the evolutionary algorithms, NSGA-II obtained the best results in both metrics.

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Author information

Authors and Affiliations

  1. Systems and Computer Engineering Department, Universidad de los Andes, Bogotá, Colombia

    Nicolás Cobos, Ixtli Barbosa, Germán A. Montoya & Carlos Lozano-Garzon

Authors
  1. Nicolás Cobos
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  2. Ixtli Barbosa
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  3. Germán A. Montoya
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  4. Carlos Lozano-Garzon
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Corresponding author

Correspondence to Carlos Lozano-Garzon .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Covenant University, Ota, Nigeria

    Sanjay Misra

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Cobos, N., Barbosa, I., Montoya, G.A., Lozano-Garzon, C. (2020). A Multi-objective Evolutionary Algorithms Approach to Optimize a Task Scheduling Problem. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-61702-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61701-1

  • Online ISBN: 978-3-030-61702-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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