In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving
some optimization problems, it faces many issues. Thus, WOA has attracted scholars' …

Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

The selection of the mutation strategy for differential evolution (DE) algorithm plays an
important role in the optimization performance, such as exploration ability, convergence …

Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

Many fields such as data science, data mining suffered from the rapid growth of data volume
and high data dimensionality. The main problems which are faced by these fields include …

One of the main issues with heuristics and meta-heuristics is the local optima stagnation
phenomena. It is often called premature convergence, which refers to the assumption of a …

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Differential Evolution (DE) is a state-of-the art global optimization technique. Considerable
research effort has been made to improve this algorithm and apply it to a variety of practical …

This study proposes a novel learning scheme for the kernel extreme learning machine
(KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed …