The Harris hawk optimizer is a recent population-based metaheuristics algorithm that
simulates the hunting behavior of hawks. This swarm-based optimizer performs the …

In this review, we intend to present a complete literature survey on the conception and
variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along …

Metaheuristics play a crucial role in solving optimization problems. The majority of such
algorithms are inspired by collective intelligence and foraging of creatures in nature. In this …

The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …

The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work.
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …

Feature selection is a required preprocess stage in most of the data mining tasks. This paper
presents an improved Harris hawks optimization (HHO) to find high-quality solutions for …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Image segmentation is one of the most significant and required procedures in pre-
processing and analyzing images. Metaheuristic optimization algorithms are used to solve a …

Feature selection, an optimization problem, becomes an important pre-process tool in data
mining, which simultaneously aims at minimizing feature-size and maximizing model …

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play
a crucial role in algorithms performance. It reduces the processing time and accuracy of the …