The workshop scheduling problems based on data mining and particle swarm optimisation algorithm in machine learning areas

The optimisation process and results are classified and stored to guide the future workshop scheduling and improve the retrieval efficiency. The results show that the random inertia weight strategy is added to the standard particle swarm optimisation (PSO) algorithm. The idea of crossover and mutati...

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Veröffentlicht in:Enterprise information systems Jg. 16; H. 2; S. 363 - 378
Hauptverfasser: Su, Yingying, Han, Lianjuan, Wang, Huimin, Wang, Jianan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 01.02.2022
Schlagworte:
genetic algorithm
Job shop scheduling
machine learning
particle swarm optimisation
ISSN:1751-7575, 1751-7583
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Zusammenfassung:The optimisation process and results are classified and stored to guide the future workshop scheduling and improve the retrieval efficiency. The results show that the random inertia weight strategy is added to the standard particle swarm optimisation (PSO) algorithm. The idea of crossover and mutation in genetic algorithm (GA) is introduced to increase the diversity of population and prevent it from falling into local optimal solution. Finally, the global optimal solution can be searched by using the strong ability of genetic algorithm to jump out of local optimal to ensure that population evolution is stagnated.
ISSN:1751-7575
1751-7583
DOI:10.1080/17517575.2019.1700551