HC-ACO: The Hyper-Cube Framework for Ant Colony Optimization. Ant Colony Optimization (ACO)  is a recently proposed metaheuristic approach for solving hard combinatorial optimization problems. The inspiring source of ACO is the foraging behavior of real ants. In most ACO implementations the hyperspace for the pheromone values used by the ants to build solutions is only implicitly limited. In this paper we propose a new way of implementing ACO algorithms, which explicitly de nes the hyperspace for the pheromone values as the convex hull of the set of 0-1 coded feasible solutions of the combinatorial optimization problem under consideration. We call this new implementation the hyper-cube framework for ACO algorithms.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Nayyar, Anand (ed.); Le, Dac-Nhuong (ed.); Nguyen, Nhu Gia (ed.): Advances in swarm intelligence for optimizing problems in computer science (2019)
- Blum, Christian: Beam-ACO--hybridizing ant colony optimization with beam search: an application to open shop scheduling (2004)
- Blum, Christian; Blesa, Maria J.: New metaheuristic approaches for the edge-weighted (k)-cardinality tree problem (2004)