Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems
Recently, the numerical optimization field has attracted the research community to propose
and develop various metaheuristic optimization algorithms. This paper presents a new …
and develop various metaheuristic optimization algorithms. This paper presents a new …
Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems
The difficulty and complexity of the real-world numerical optimization problems has grown
manifold, which demands efficient optimization methods. To date, various metaheuristic …
manifold, which demands efficient optimization methods. To date, various metaheuristic …
Jaya algorithm and applications: A comprehensive review
All the population-based optimization algorithms are probabilistic algorithms and require
only a few control parameters including number of candidates, number of iterations, elite …
only a few control parameters including number of candidates, number of iterations, elite …
An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm
EH Houssein, MM Emam, AA Ali - Neural Computing and Applications, 2022 - Springer
Breast cancer is the second leading cause of death in women; therefore, effective early
detection of this cancer can reduce its mortality rate. Breast cancer detection and …
detection of this cancer can reduce its mortality rate. Breast cancer detection and …
Boosted sooty tern optimization algorithm for global optimization and feature selection
Feature selection (FS) represents an optimization problem that aims to simplify and improve
the quality of highly dimensional datasets through selecting prominent features and …
the quality of highly dimensional datasets through selecting prominent features and …
GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems
In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation
A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced
using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency …
using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency …
Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images
EH Houssein, Z Abohashima… - Journal of …, 2022 - academic.oup.com
Despite the great efforts to find an effective way for coronavirus disease 2019 (COVID-19)
prediction, the virus nature and mutation represent a critical challenge to diagnose the …
prediction, the virus nature and mutation represent a critical challenge to diagnose the …
Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems
Abstract The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the
oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may …
oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may …
Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
EH Houssein, MM Emam, AA Ali - Neural Computing and Applications, 2021 - Springer
Abstract Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to
people's safety and health. Many algorithms were developed to identify COVID-19. One way …
people's safety and health. Many algorithms were developed to identify COVID-19. One way …