Feature selection (FS) is one of the most critical tasks in data mining, which aims to reduce
the dimensionality of the data and maximize classification accuracy. The FS problem can be …

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization
problems in various fields. These algorithms have become popular because of their ability to …

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs)
resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike …

Abstract Portable Document Format (PDF) is a file format created to create portable and
printable documents across platforms. PDF files are one of the most widely used application …

Advances in PV technology have given rise to the increasing integration of PV-based
distributed generation (PVDG) systems into distribution systems to mitigate the dependence …

Optimization problem involving interval parameters and multiple conflicting objectives are
called multi-objective optimization problems with interval parameters (IMOPs), which are …

The reliability of key components of urban rail transit (URT) plays an important role in the
maintenance plans of URT. It is necessary to establish the reliability model of URT trains. In …

Data clustering is a machine learning method for unsupervised learning that is popular in
the two areas of data analysis and data mining. The objective is to partition a given dataset …

In an era inundated with high-dimensional consumer data, the need for advanced hyper-
dimensional pattern analysis poses a significant computational challenge. This research …

Due to the growing interest in green logistics and the challenge of just-in-time part logistics,
as well as considering the current popularity of supply hub, this paper focuses on a supply …