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 …

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in
image segmentation that can efficiently resolve difficulties aroused while analyzing the …

With the development of modern sensor technology, the automatic movement of robot has
become a reality, and improving the path planning performance of robot in dynamic and …

Feature Selection (FS) is considered a crucial procedure for eliminating unnecessary
features from datasets. FS is considered a challenging problem that is difficult to solve …

The initiative to introduce new benchmark problems has drawn attention to the development
of new optimization algorithms. Recently, a set of constrained benchmark problems has …

The issue of network security is an important and delicate issue when it comes to the privacy
of organizations and individuals, especially when important and sensitive information is …

To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the
most challenging task. In this paper, performance of the various developed meta-heuristic …

Feature Selection (FS) is an essential process that is implicated in data mining and machine
learning for data preparation by removing redundant and irrelevant features, thereby falling …

Optimization problems are prevalent in a variety of real-world applications, including
medical, engineering, chemical, and others, and must be precisely solved to enhance the …

Humans take immense pride in their ability to be unpredictably intelligent and despite huge
advances in science over the past century; our understanding about human brain is still far …