Meta-heuristic algorithms have shown promising performance in solving sophisticated real-
world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering …

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial
Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has …

Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of
applications because of their strong capabilities in picking the optimal features and …

Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims
to remove redundant and uncorrelated features, thus improving the accuracy of models …

Selecting a subset of candidate features is one of the important steps in the data mining
process. The ultimate goal of feature selection is to select an optimal number of high-quality …

The input layer, hidden layer, and output layer are three models of the neural processors
that make up feedforward neural networks (FNNs). Evolutionary algorithms have been …

Feature Selection (FS) is an important data management technique that aims to minimize
redundant information in a dataset. This work proposes DENGO, an improved version of the …

Identification of the optimal subset of features for Feature Selection (FS) problems is a
demanding problem in machine learning and data mining. A trustworthy optimization …

The main objective of this paper is to introduce a new NIO algorithm inspired from the
hunting strategy of the leopard seals called Leopard Seal Optimization (LSO) to provide a …

Abstract The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that
takes its cues from the collective intelligence of wild gorilla troops. Although it has shown …