Ant Colony Optimization:An overview

Ant Colony Optimization: an Overview

Ant Colony Optimization: A New Meta-Heuristic

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief ove...

The Use of Ant Colony Inspired Methods in Electrocardiogram Interpretation, an Overview

This paper presents an overview of methods inspired by the behaviour of real ants in the nature which are currently in the focus of research and which have been used in the process of electrocardiogram interpretation and processing. The paper describes the use of dynamic time warping algorithm used together with ant colony inspired clustering and considers the relevant steps to speed up the alg...

Ant Colony Optimization: Overview and Recent Advances

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 solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly a...

A Concise Overview of Applications of Ant Colony Optimization

Ant colony optimization (ACO) [1–3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic inf...