Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving
some optimization problems, it faces many issues. Thus, WOA has attracted scholars' …

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 recent years, several metaheuristic algorithms have been introduced in engineering and
scientific fields to address real-life optimization problems. In this study, a novel nature …

Recently, many metaheuristic optimization algorithms have been developed to address real-
world issues. In this study, a new physics-based metaheuristic called Fick's law optimization …

Attempting to address optimization problems in various scientific disciplines is a
fundamental and significant difficulty requiring optimization. This study presents the …

The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Abstract Whale Optimization Algorithm (WOA) is an optimization algorithm developed by
Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the …

Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic
algorithm that is based on the bubble-net hunting maneuver technique—of humpback …

The use of metaheuristics is one of the most encouraging methodologies for taking care of
real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence …