Abstract Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone
trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic …

This article presents an overview of recent work on ant algorithms, that is, algorithms for
discrete optimization that took inspiration from the observation of ant colonies' foraging …

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving
systems with sophisticated collective intelligence. Composed of simple interacting agents …

Swarm intelligence is a relatively new approach to problem solving that takes inspiration
from the social behaviors of insects and of other animals. In particular, ants have inspired a …

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have
been applied to the solution of difficult discrete optimization problems. We put these …

Research on a new metaheuristic for optimization is often initially focused on proof-of-
concept applications. It is only after experimental work has shown the practical interest of the …

There is currently no standard or widely accepted subset of features to effectively classify
different emotions based on electroencephalogram (EEG) signals. While combining all …

In this paper we present an extension of ant colony optimization (ACO) to continuous
domains. We show how ACO, which was initially developed to be a metaheuristic for …

Ant colony optimization is a technique for optimization that was introduced in the early
1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant …

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in
communications networks. AntNet is a distributed, mobile agents based Monte Carlo system …