An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
A systematic review on metaheuristic optimization techniques for feature selections in disease diagnosis: open issues and challenges
There is a need for some techniques to solve various problems in today's computing world.
Metaheuristic algorithms are one of the techniques which are capable of providing practical …
Metaheuristic algorithms are one of the techniques which are capable of providing practical …
Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems
This work presents a novel nature-inspired metaheuristic called Nutcracker Optimization
Algorithm (NOA) inspired by Clark's nutcrackers. The nutcrackers exhibit two distinct …
Algorithm (NOA) inspired by Clark's nutcrackers. The nutcrackers exhibit two distinct …
Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems
B Abdollahzadeh… - … Journal of Intelligent …, 2021 - Wiley Online Library
Metaheuristics play a critical role in solving optimization problems, and most of them have
been inspired by the collective intelligence of natural organisms in nature. This paper …
been inspired by the collective intelligence of natural organisms in nature. This paper …
African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems
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 …
algorithms are inspired by collective intelligence and foraging of creatures in nature. In this …
Multiclass feature selection with metaheuristic optimization algorithms: a review
Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …
selection is harder to perform since most classifications are binary. The feature selection …
Metaheuristics: a comprehensive overview and classification along with bibliometric analysis
Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …
overload of wide range of available metaheuristic-based solution approaches. Since the …
[HTML][HTML] A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems
Activity-based scheduling optimization is a combinatorial problem built on the traveling
salesman problem intending to optimize people schedules considering their trips and the …
salesman problem intending to optimize people schedules considering their trips and the …
Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)
Today, intelligent optimization has become a science that few researchers have not used in
dealing with problems in their field. Diversity and flexibility have made the use, efficiency …
dealing with problems in their field. Diversity and flexibility have made the use, efficiency …
DL‐IDS: a deep learning–based intrusion detection framework for securing IoT
Abstract The Internet of Things (IoT) is comprised of numerous devices connected through
wired or wireless networks, including sensors and actuators. Recently, the number of IoT …
wired or wireless networks, including sensors and actuators. Recently, the number of IoT …