Ant Colony Optimization: A Component-Wise Overview

Manuel López-Ibáñez (), Thomas Stützle () and Marco Dorigo ()
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Manuel López-Ibáñez: University of Manchester, Alliance Manchester Business School
Thomas Stützle: IRIDIA, Université Libre de Bruxelles (ULB)
Marco Dorigo: IRIDIA, Université Libre de Bruxelles (ULB)

Chapter 13 in Handbook of Heuristics, 2018, pp 371-407 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-07124-4_21

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