The ACO encoding

Authors:
Alberto Moraglio;Fernando E. B. Otero;Colin G. Johnson
Affiliations:
School of Computing and Centre for Reasoning, University of Kent, Canterbury, UK;School of Computing and Centre for Reasoning, University of Kent, Canterbury, UK;School of Computing and Centre for Reasoning, University of Kent, Canterbury, UK
Venue:
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Year:
2010

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Abstract

Ant Colony Optimization (ACO) differs substantially from other meta-heuristics such as Evolutionary Algorithms (EA). Two of its distinctive features are: (i) it is constructive rather than based on iterative improvements, and (ii) it employs problem knowledge in the construction process via the heuristic function, which is essential for its success. In this paper, we introduce the ACO encoding, which is a self-contained algorithmic component that can be readily used to make available these two particular features of ACO to any search algorithm for continuous spaces based on iterative improvements to solve combinatorial optimization problems.