Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer
(WSO) to solve optimization problems over a continuous search space. The core ideas and …

In recent years, artificial neural network (ANN) is one of the popular and effective machine
learning models that can be used to accurately predict fiber reinforced polymer (FRP) …

Metaheuristics play a critical role in solving optimization problems, and most of them have
been inspired by the collective intelligence of natural organisms in nature. This paper …

The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers
are some of the reasons for their high popularity and acceptance for control in process …

Metaheuristics play a crucial role in solving optimization problems. The majority of such
algorithms are inspired by collective intelligence and foraging of creatures in nature. In this …

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers.
Most of these cliché methods mimic animals' searching trends and possess a small …

A recent set of overused population-based methods have been published in recent years.
Despite their popularity, most of them have uncertain, immature performance, partially done …

Currently, meta-heuristic algorithms have been widely studied and applied, but balancing
exploration and exploitation remains a challenge. In this study, a novel meta-heuristic …