Ant Colony Optimization: Overview and Recent Advances

Marco Dorigo () and Thomas Stützle
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Marco Dorigo: Université Libre de Bruxelles (ULB)
Thomas Stützle: Université Libre de Bruxelles (ULB)

Chapter Chapter 10 in Handbook of Metaheuristics, 2019, pp 311-351 from Springer

Abstract: Abstract 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 available heuristic information. Since the proposal of Ant System, the first ACO algorithm, many significant research results have been obtained. These contributions focused on the development of high performing algorithmic variants, the development of a generic algorithmic framework for ACO algorithm, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of important properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.

Keywords: Pheromone Trail; Heuristic Information; Solution Construction; Approximate Nondeterministic Tree Search (ANTS); Single Machine Total Weighted Tardiness Problem (SMTWTP) (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-319-91086-4_10

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