Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality”
when dealing with high-dimensional data. Focusing on this challenge, this article studies a …

DE algorithms have outstanding performance in solving complex problems. However, they
also have highlighted the need for an effective approach to alleviating the risk of premature …

The deployment of a very large number of readers in a limited space may increase the
probability of collision among radio-frequency identification (RFID) readers and reduce the …

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input
membership degrees, which lacks expressiveness and flexibility. In this article, a novel …

Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is
an extension of the traditional FJS, which is harder to solve. This work aims to minimize total …

Meta-heuristic search algorithms (MHSs) are methods that take their inspiration from nature.
However, the fitness value information used in the design of the update mechanism in MHSs …

The flowshop sequence-dependent group scheduling problem (FSDGSP) with the
production efficiency measures has been extensively studied due to its wide industrial …