Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

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 …

In recent years, several metaheuristic algorithms have been introduced in engineering and
scientific fields to address real-life optimization problems. In this study, a novel nature …

This study proposes a new metaheuristic algorithm called sand cat swarm optimization
(SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are …

Dandelion Optimizer (DO) is a recently proposed swarm intelligence algorithm that
coincides with the process of finding the best reproduction site for dandelion seeds …

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search
Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of …

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …

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 …

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image
segmentation technology. We present a new multilevel image segmentation method based …

This paper proposes a novel population-based optimization method, called Aquila Optimizer
(AO), which is inspired by the Aquila's behaviors in nature during the process of catching the …