Quantum-inspired metaheuristic algorithms: comprehensive survey and classification
FS Gharehchopogh - Artificial Intelligence Review, 2023 - Springer
Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …
optimization. These algorithms supply powerful instruments with significant engineering …
Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
Slime mould algorithm: A new method for stochastic optimization
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is
proposed based on the oscillation mode of slime mould in nature. The proposed SMA has …
proposed based on the oscillation mode of slime mould in nature. The proposed SMA has …
Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique
The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province,
China in late December 2019, is rapidly spreading and affecting all countries in the world …
China in late December 2019, is rapidly spreading and affecting all countries in the world …
Mean–variance portfolio optimization using machine learning-based stock price prediction
W Chen, H Zhang, MK Mehlawat, L Jia - Applied Soft Computing, 2021 - Elsevier
The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …
markets. Recent developments in machine learning have brought significant opportunities to …
A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications
L Abualigah, A Diabat - Neural Computing and Applications, 2020 - Springer
The grasshopper optimization algorithm is one of the dominant modern meta-heuristic
optimization algorithms. It has been successfully applied to various optimization problems in …
optimization algorithms. It has been successfully applied to various optimization problems in …
Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
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 …
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …
Salp swarm algorithm: a comprehensive survey
This paper completely introduces an exhaustive and a comprehensive review of the so-
called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of …
called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of …
[PDF][PDF] Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets
In data mining and machine learning, feature selection is a critical part of the process of
selecting the optimal subset of features based on the target data. There are 2n potential …
selecting the optimal subset of features based on the target data. There are 2n potential …
Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts
Accurate estimation of critical parameters of the photovoltaic models is a highly demanding
task in modeling and simulating the photovoltaic systems. In this research, a diversification …
task in modeling and simulating the photovoltaic systems. In this research, a diversification …