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Ant Colony Optimization: A Component-Wise Overview

In: Handbook of Heuristics

Author

Listed:
  • Manuel López-Ibáñez

    (University of Manchester, Alliance Manchester Business School)

  • Thomas Stützle

    (Université Libre de Bruxelles (ULB), IRIDIA)

  • Marco Dorigo

    (Université Libre de Bruxelles (ULB), IRIDIA)

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.

Suggested Citation

  • Manuel López-Ibáñez & Thomas Stützle & Marco Dorigo, 2025. "Ant Colony Optimization: A Component-Wise Overview," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G.C. Resende (ed.), Handbook of Heuristics, edition 0, chapter 19, pages 543-581, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-00385-0_21
    DOI: 10.1007/978-3-032-00385-0_21
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