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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References
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). https://doi.org/10.1007/978-1-4613-0303-9_14
Xia, W., Shen, L.: Joint resource allocation using evolutionary algorithms in heterogeneous mobile cloud computing networks. China Commun. 15(8), 189–204 (2018)
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)
Center for Computing Research at Sandia National Laboratories: Pyomo. http://www.pyomo.org/
Khaos Investigación. JMetal. https://jmetal.github.io/jMetal/
Hart, W.E., et al.: Pyomo-Optimization Modeling in Python, 2nd edn. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-58821-6
Durillo, J., Nebro, A.: JMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, West Sussex (2001)
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)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Institut für Technische Informatik und Kommunikationsnetze (TIK) 103, 5–6 (2001)
Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Advanced Information and Knowledge Processing Series. Springer, Heidelberg (2004). https://doi.org/10.1007/1-84628-132-6
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)
Chicano, F., Sutton, A., Whitley, L., Alba, E.: Fitness probability distribution of bit-flip mutation. Evol. Comput. 23(2), 217–248 (2014)
Mavrotas, G.: Effective implementation of the e-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213, 455–465 (2009)
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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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