This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II
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Huacuja, Hé. J. F., Santiago, A., Pecero, J. E., Dorronsoro, B., Bouvry, P., Monterrubio, J. C. S., … Santillan, C. Góm. (2015). A comparison between memetic algorithm and seeded genetic algorithm for multi-objective independent task scheduling on heterogeneous machines. Studies in Computational Intelligence, 601, 377–389. https://doi.org/10.1007/978-3-319-17747-2_29
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