The field of parallel metaheuristics is continuously evolving as a result of new technologies
and needs that researchers have been encountering. In the last decade, new models of …

Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the
social behavior of ant colonies for solving optimization problems. When facing large and …

Classification using Ant Programming is a challenging data mining task which demands a
great deal of computational resources when handling data sets of high dimensionality. This …

This paper presents an integrated modeling method for multi-criteria land-use suitability
assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation …

Classification is an important data mining task which facilitates list of decision rules that
helps us to predict class of an unseen instance. Various traditional techniques like Decision …

In this paper a survey on existing distributed approaches to swarm intelligence approaches
for graph search problems is presented. In particular we reviewed papers on Ant Colony …

This study implements parallelization of Ant-Miner for classification rules discovery. Ant-
Miner code is parallelized and optimized in a cluster environment by employing master …

Enormous studies on intrusion detection have widely applied data mining techniques to
finding out the useful knowledge automatically from large amount of databases, while few …

Ant colony optimisation (ACO) algorithms have been used to discover accurate, and
comprehensible classification rules. Discovering exceptions using ACO is an underexplored …

In this chapter we present some concepts pertaining to a hybrid approach to classification
and clustering. Hybridization amounts to combining standard algorithms, such as those …