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Published in 2019 at "Cluster Computing"
DOI: 10.1007/s10586-019-02934-0
Abstract: Scheduling is a process of mapping resources to tasks and it’s objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are…
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Keywords:
energy consumption;
parallel genetic;
hybrid dual;
multiprocessor scheduling ... See more keywords
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Published in 2020 at "Evolving Systems"
DOI: 10.1007/s12530-020-09337-2
Abstract: Industrial optimization problems are usually difficult to solve due to complexity and high number of constraints. Evolutionary algorithms are a conventional method to solve these problems. However, many industrial applications are real-time or we need…
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Keywords:
master;
asynchronous distributed;
parallel genetic;
distributed multi ... See more keywords
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Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-93943-0
Abstract: Unleashing the potential of large-scale data analysis requires advanced computational methods capable of managing the immense size and complexity of distributed data. Genetic algorithms (GAs), known for their adaptability, benefit significantly from parallelization, prompting ongoing…
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Keywords:
automated parallel;
parallel genetic;
analysis;
distributed data ... See more keywords
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Published in 2025 at "Bulletin of Electrical Engineering and Informatics"
DOI: 10.11591/eei.v14i2.6195
Abstract: Flexible job scheduling problem (JSP) as an optimization problem, tends to find solution for allowing different operations to be processed faster. This problem could be solved by genetic algorithm, as we have proven in another…
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Keywords:
flexible scheduling;
parallel genetic;
algorithm;
genetic algorithm ... See more keywords