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

• Published in: Enterprise information systems Vol. 16; no. 2; pp. 363 - 378
• Main Authors: Su, Yingying, Han, Lianjuan, Wang, Huimin, Wang, Jianan
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 01.02.2022
• Subjects: genetic algorithm; Job shop scheduling; machine learning; particle swarm optimisation
• ISSN: 1751-7575; 1751-7583
• DOI: 10.1080/17517575.2019.1700551

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.