Published in

Taylor and Francis Group, Cybernetics and Systems, 4(39), p. 395-424

DOI: 10.1080/01969720802039560

Links

Tools

Export citation

Search in Google Scholar

Very Strongly Constrained Problems: An Ant Colony Optimization Approach

Journal article published in 2006 by Vittorio Maniezzo, Matteo Roffilli ORCID
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Red circle
Preprint: archiving forbidden
Orange circle
Postprint: archiving restricted
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This paper is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The second outlines current research within the ACO community, reporting recent results obtained on different problems, while the third part focuses on a particular research line, named ANTS, providing some details on the algorithm and presenting results recently obtained on a prototypical strongly constrained problem: the set partitioning problem.