Particle swarm optimization (PSO) has been widely applied in solving optimization
problems. Despite a multitude of PSO variants that have been proposed thus far, they still …

With the development of the economy, distributed manufacturing has gradually become the
mainstream production mode. This work aims to solve the energy-efficient distributed flexible …

Feature selection is a critical preprocessing step in machine learning with significant real-
world applications. Despite the widespread use of particle swarm optimization (PSO) for …

Abstract Particle Swarm Optimization (PSO) is a key tool in Artificial Intelligence, is well-
known to the public for its effectiveness in addressing complex and diverse problems. It …

Numerical metaheuristics are often tested on mathematical problems collected into a
benchmark set. There are many benchmark sets, but the number of problems in a particular …

The evolutionary algorithm has become a major method for neural architecture search
recently. However, the fixed probability distribution employed by the traditional evolutionary …

The grey prediction model is a scientific and effective prediction method for small amounts of
incomplete data. In this paper, we proposed a particle swarm optimization for mixed mutant …

CGP has often been applied for neural architecture search (NAS). However, the
performance of Cartesian genetic programming (CGP) is less than ideal when searching for …

The ant colony algorithm faces dimensional catastrophe problems when solving the large-
scale traveling salesman problem, which leads to unsatisfactory solution quality and …

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …