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

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


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.


  • Multi-Objective Evolutionary Algorithms
  • Multi-objective optimization
  • SPEA2
  • Task scheduling

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  • DOI: 10.1007/978-3-030-61702-8_21
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  1. Katoh, N., Shioura, A., Ibaraki, T.: Resource allocation problems. In: Pardalos, P., Du, D.Z., Graham, R. (eds.) Handbook of Combinatorial Optimization. Springer, New York (2013).

  2. Xia, W., Shen, L.: Joint resource allocation using evolutionary algorithms in heterogeneous mobile cloud computing networks. China Commun. 15(8), 189–204 (2018)

    CrossRef  Google Scholar 

  3. Zhou, J., Zhao, X., Zhang, X., Zhao, D., Li, H.: Task allocation for multi-agent systems based on distributed many-objective evolutionary algorithm and greedy algorithm. IEEE Access 8, 19306–19318 (2020)

    CrossRef  Google Scholar 

  4. Center for Computing Research at Sandia National Laboratories: Pyomo.

  5. Khaos Investigación. JMetal.

  6. Hart, W.E., et al.: Pyomo-Optimization Modeling in Python, 2nd edn. Springer, Heidelberg (2017).

    CrossRef  MATH  Google Scholar 

  7. Durillo, J., Nebro, A.: JMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)

    CrossRef  Google Scholar 

  8. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, West Sussex (2001)

    MATH  Google Scholar 

  9. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    CrossRef  Google Scholar 

  10. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Institut für Technische Informatik und Kommunikationsnetze (TIK) 103, 5–6 (2001)

    Google Scholar 

  11. Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Advanced Information and Knowledge Processing Series. Springer, Heidelberg (2004).

    CrossRef  MATH  Google Scholar 

  12. Rahman, R., Ramli, R., Jamari, Z., Ku-Mahamud, K.: Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation. Hindawi Publishing Corporation, London (2016)

    CrossRef  Google Scholar 

  13. Chicano, F., Sutton, A., Whitley, L., Alba, E.: Fitness probability distribution of bit-flip mutation. Evol. Comput. 23(2), 217–248 (2014)

    CrossRef  Google Scholar 

  14. Mavrotas, G.: Effective implementation of the e-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213, 455–465 (2009)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Carlos Lozano-Garzon .

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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.

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