Monarch butterfly optimization: a comprehensive review
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or
artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm …
artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm …
The monarch butterfly optimization algorithm for solving feature selection problems
M Alweshah, SA Khalaileh, BB Gupta… - Neural Computing and …, 2022 - Springer
Feature selection (FS) is considered to be a hard optimization problem in data mining and
some artificial intelligence fields. It is a process where rather than studying all of the features …
some artificial intelligence fields. It is a process where rather than studying all of the features …
Monarch butterfly optimization based convolutional neural network design
Convolutional neural networks have a broad spectrum of practical applications in computer
vision. Currently, much of the data come from images, and it is crucial to have an efficient …
vision. Currently, much of the data come from images, and it is crucial to have an efficient …
Feature selection using binary monarch butterfly optimization
L Sun, S Si, J Zhao, J Xu, Y Lin, Z Lv - Applied Intelligence, 2023 - Springer
Swarm intelligence algorithms have superior performance in searching for the optimal
feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous …
feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous …
Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem
As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack
problem (DKP) is proposed based on the concept of discount in the commercial world. The …
problem (DKP) is proposed based on the concept of discount in the commercial world. The …
Current best opposition-based learning salp swarm algorithm for global numerical optimization
The salp swarm algorithm is one of the novel swarm intelligence metaheuristics. The work
proposed in this paper provides further improvements of the salp swarm algorithm, that have …
proposed in this paper provides further improvements of the salp swarm algorithm, that have …
A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm
WAHM Ghanem, A Jantan - Neural Computing and Applications, 2020 - Springer
The most pressing issue in network security is the establishment of an approach that is
capable of detecting violations in computer systems and networks. There have been several …
capable of detecting violations in computer systems and networks. There have been several …
Solving feature selection problems by combining mutation and crossover operations with the monarch butterfly optimization algorithm
M Alweshah - Applied Intelligence, 2021 - Springer
Feature selection (FS) is used to solve hard optimization problems in artificial intelligence
and data mining. In the FS process, some, rather than all of the features of a dataset are …
and data mining. In the FS process, some, rather than all of the features of a dataset are …
An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems
MA Tawhid, AM Ibrahim - Soft Computing, 2023 - Springer
This article proposes a new hybrid swarm intelligence optimization algorithm called
monarch butterfly optimization (MBO) algorithm with cuckoo search (CS) algorithm, named …
monarch butterfly optimization (MBO) algorithm with cuckoo search (CS) algorithm, named …
BSSFS: binary sparrow search algorithm for feature selection
L Sun, S Si, W Ding, J Xu, Y Zhang - International Journal of Machine …, 2023 - Springer
Swarm intelligence algorithms can efficiently solve feature selection optimization problems
for classification, and their classification performance is also excellent. The Sparrow Search …
for classification, and their classification performance is also excellent. The Sparrow Search …