Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

In this article, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced
with detailed discussions, which is intended to keep researchers interested in swarm …

The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy
to understand and has a strong optimization capability. However, the SMA is not suitable for …

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the
cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce …

Feature selection is an NP-hard problem to remove irrelevant and redundant features with
no predictive information to increase the performance of machine learning algorithms. Many …

The recent advancements in science, engineering, and technology have facilitated huge
generation of datasets. These huge datasets contain noisy, redundant, and irrelevant …

This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …

Feature selection represents an essential pre-processing step for a wide range of Machine
Learning approaches. Datasets typically contain irrelevant features that may negatively …

The importance of medical data and the crucial nature of the decisions that are based on
such data, as well as the large increase in its volume, has encouraged researchers to …

The strong volatility and randomness of wind power impact the grid and reduce the voltage
quality of the grid when wind power is connected to the grid in large scale. The power sector …