This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …

A large number of metaheuristics inspired by natural and social phenomena have been
proposed in the last few decades, each trying to be more powerful and innovative than …

Although a significant advancement of differential evolution (DE) for global optimization has
been witnessed in the past two decades, the problems of premature convergence and …

STNWeb is a new web tool for the visualization of the behavior of optimization algorithms
such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple …

We consider the problem of visualizing the population dynamics along an evolutionary run
using a dimensionality reduction technique for mapping individuals from the original search …

Despite the recent success in a plethora of hyperparameter optimization (HPO) methods for
machine learning (ML) models, the intricate interplay between model hyperparameters …

The computation resource allocation is a key issue to the multiobjective evolutionary
algorithms. Present studies still have some difficulties addressing this issue such as low …

Evolutionary algorithms (EAs) are efficient computational intelligence (CI) techniques for
solving complex optimization problems in different areas. Explainable artificial intelligence …

Manifold learning techniques have become increasingly valuable as data continues to grow
in size. By discovering a lower-dimensional representation (embedding) of the structure of a …

Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that
has been successfully used to solve a number of hard optimization problems. It has been …