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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 …
Ant lion optimizer: a comprehensive survey of its variants and applications
This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a
novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the …
novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the …
Ant lion optimization: variants, hybrids, and applications
Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that
simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge …
simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge …
[HTML][HTML] Multi-swarm algorithm for extreme learning machine optimization
There are many machine learning approaches available and commonly used today,
however, the extreme learning machine is appraised as one of the fastest and, additionally …
however, the extreme learning machine is appraised as one of the fastest and, additionally …
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction
Accurate and reliable prediction of streamflow is vital to the optimization of water resources
management, reservoir flood operations, catchment, and urban water management. In this …
management, reservoir flood operations, catchment, and urban water management. In this …
[HTML][HTML] The orb-weaving spider algorithm for training of recurrent neural networks
AS Mikhalev, VS Tynchenko, VA Nelyub, NM Lugovaya… - Symmetry, 2022 - mdpi.com
The quality of operation of neural networks in solving application problems is determined by
the success of the stage of their training. The task of learning neural networks is a complex …
the success of the stage of their training. The task of learning neural networks is a complex …
Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches
In this research, five hybrid novel machine learning approaches, artificial neural network
(ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO) …
(ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO) …
Chaotic harris hawks optimization with quasi-reflection-based learning: An application to enhance cnn design
The research presented in this manuscript proposes a novel Harris Hawks optimization
algorithm with practical application for evolving convolutional neural network architecture to …
algorithm with practical application for evolving convolutional neural network architecture to …
Forecast and prediction of COVID-19 using machine learning
COVID-19 outbreaks only affect the lives of people, they result in a negative impact on the
economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the …
economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the …
[HTML][HTML] A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend
This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …