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 …

The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

Simple Summary This paper proposes a deep learning-based skin cancer classification
network (DSCC_Net) that is based on a convolutional neural network (CNN) and …

Abstract The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that
was shown to have excellent performance for numerous optimization problems. In this …

This research presents a new model for finding optimal conditions in the concrete
technology area. To do that, results of a series of laboratory investigations on concrete …

Abstract Particle Swarm Optimization (PSO) is one of the most widely used heuristic
algorithms. The simplicity and inexpensive computational cost makes this algorithm very …

In the feature selection process, reaching the best subset of features is considered a difficult
task. To deal with the complexity associated with this problem, a sophisticated and robust …

Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired
by chimps' individual intelligence and sexual motivation in their group hunting. The …

Since most metaheuristic algorithms for continuous search space have been developed, a
number of transfer functions have been proposed including S-shaped, V-shaped, linear, U …

This study introduces a new model to determine the critical flyrock event in mines. The
flyrock was predicted and optimized using a field database including six parameters and …