Higher Order Pheromone Models in Ant Colony Optimisation

  • James Montgomery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


Ant colony optimisation is a constructive metaheuristic that successively builds solutions from problem-specific components. A parameterised model known as pheromone—an analogue of the trail pheromones used by real ants—is used to learn which components should be combined to produce good solutions. In the majority of the algorithm’s applications a single parameter from the model is used to influence the selection of a single component to add to a solution. Such a model can be described as first order. Higher order models describe relationships between several components in a solution, and may arise either by contriving a model that describes subsets of components from a first order model or because the characteristics of solutions modelled naturally relate subsets of components. This paper introduces a simple framework to describe the application of higher order models as a tool to understanding common features of existing applications. The framework also serves as an introduction to those new to the use of such models. The utility of higher order models is discussed with reference to empirical results in the literature.


Order Model Travel Salesman Problem Partial Solution Constraint Satisfaction Problem Solution Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • James Montgomery
    • 1
  1. 1.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia

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