Future Generation Computer Systems
Model-Based Search for Combinatorial Optimization: A Comparative Study
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization For The Edge-weighted k-cardinality Tree Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Searching for maximum cliques with ant colony optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
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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.