Living Reference Work Entry

Handbook of Heuristics

pp 1-37

Date: Latest Version

Ant Colony Optimization: A Component-Wise Overview

  • Manuel López-IbáñezAffiliated withAlliance Manchester Business School, University of Manchester Email author 
  • , Thomas StützleAffiliated withIRIDIA, Université libre de Bruxelles(ULB), CoDE, CP 194/6
  • , Marco DorigoAffiliated withIRIDIA, Université libre de Bruxelles(ULB), CoDE, CP 194/6


The indirect communication and foraging behavior of certain species of ants have inspired a number of optimization algorithms for NP-hard problems. These algorithms are nowadays collectively known as the ant colony optimization (ACO) metaheuristic. This chapter gives an overview of the history of ACO, explains in detail its algorithmic components, and summarizes its key characteristics. In addition, the chapter introduces a software framework that unifies the implementation of these ACO algorithms for two example problems, the traveling salesman problem and the quadratic assignment problem. By configuring the parameters of the framework, one can combine features from various ACO algorithms in novel ways. Examples on how to find a good configuration automatically are given in the chapter. The chapter closes with a review of combinations of ACO with other techniques and extensions of the ACO metaheuristic to other problem classes.


Ant colony optimization Automatic configuration Combinatorial optimization Metaheuristics