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Ant Colony Optimization: A Component-Wise Overview

  • Manuel López-Ibáñez
  • Thomas Stützle
  • Marco Dorigo
Living reference work entry

Abstract

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.

Keywords

Ant colony optimization Automatic configuration Combinatorial optimization Metaheuristics 

Notes

Acknowledgements

The research leading to the results presented in this chapter has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement no246939. Thomas Stützle and Marco Dorigo acknowledge support of the F.R.S.-FNRS of which they are a senior research associate and a research director, respectively.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 2
  • Marco Dorigo
    • 3
  1. 1.Alliance Manchester Business SchoolUniversity of ManchesterManchesterUK
  2. 2.IRIDIAUniversité libre de Bruxelles(ULB), CoDE, CP 194/6BrusselsBelgium
  3. 3.IRIDIAUniversité libre de Bruxelles(ULB), CoDE, CP 194/6BrusselsBelgium

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