With the rapid advancements in technology, what will football look like in 2036? A decade ago, it would have been ...

Abstract: Learning differential evolution (DE) algorithms are widely adopted to address flexible job-shop scheduling problems (FJSPs) because of the optimization ability. However, traditional learning ...

Abstract: Accurate identification and positioning of multiple vehicles is a critical challenge in autonomous driving, particularly over long distances. While sparse Bayesian learning (SBL) methods ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed to solve complex and computationally expensive multiobjective optimization problems (EMOPs) in recent years.

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Abstract: The tunicate swarm algorithm (TSA) is a new optimization algorithm. Its inspiration comes from the swarm behavior of the tunicate organisms in the deep sea. Its advantages are simple ...

Abstract: As the foundation of next-generation wireless networks, distributed learning (DL) is expected to be integrated into 6 G communication networks, profoundly advancing the transformation of ...