Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has
gained prominence in the last two decades due to its ease of application in unsupervised …

The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

Particle swarm optimization (PSO) is a popular heuristic method, which is capable of
effectively dealing with various optimization problems. A detailed overview of the original …

Feature selection (FS) is a machine learning process commonly used to reduce the high
dimensionality problems of datasets. This task permits to extract the most representative …

The improvement of enterprise competitiveness depends on the ability to match segmented
customers in a competitive market. In this study, we propose a customer segmentation …

Nature has always been a source of inspiration. Over the last few decades, it has stimulated
many successful algorithms and computational tools for dealing with complex and …

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …

Abstract A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that
simulates the flash pattern and characteristics of fireflies. Clustering is a popular data …

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system
(ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid …

The general purpose optimization method known as Particle Swarm Optimization (PSO) has
a number of parameters that determine its behaviour and efficacy in optimizing a given …