Evolutionary Models applied to Multiprocessor TaskScheduling: Serial and Multipopulation Genetic Algorithm

Bruno Well Dantas Morais, Gina Maira Barbosa de Oliveira, Tiago Ismailer de Carvalho


This work presents the development of a multipopulation genetic algorithm for the task schedulingproblem with communication costs, aiming to compare its performance with the serial genetic algorithm. For thispurpose, a set of instances was developed and different approaches for genetic operations were compared.Experiments were conducted varying the number of populations and the number of processors available forscheduling. Solution quality and execution time were analyzed, and results show that the AGMP with adjustedparameters generally produces better solutions while requiring less execution time.


multipopulation genetic algorithm; multiprocessor task scheduling

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DOI: https://doi.org/10.22456/2175-2745.82412

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