A very significant field of research that keeps getting better at tackling optimization issues is
the metaheuristic algorithm. One of the categories of metaheuristic algorithms that has …

This paper proposes an improved variant of the arithmetic optimization algorithm (AOA),
called LMRAOA, which is used to solve numerical and engineering problems. Various …

There are many classic highly complex optimization problems in the world, therefore, it is still
necessary to find an applicable and effective algorithm to solve these problems. In this …

Traditional neural networks used gradient descent methods to train the network structure,
which cannot handle complex optimization problems. We proposed an improved grey wolf …

This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks
Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA …

This paper proposes a feature selection method based on a hybrid optimization algorithm
that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The …

Feature selection (FS) is one of the most critical tasks in data mining, which aims to reduce
the dimensionality of the data and maximize classification accuracy. The FS problem can be …

In biomedical data mining, the gene dimension is often much larger than the sample size. To
solve this problem, we need to use a feature selection algorithm to select feature gene …

The utilization of evolutionary machine learning has demonstrated efficacy in addressing
challenges related to medical data mining. Medical data mining is an important branch of …

Feature selection (FS) is used to reduce the dimensionality of datasets, which employs the
most informative features to obtain the maximum classification accuracy. The swarm …