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Chapitre D'ouvrage Année : 2006

An Ant Colony Optimization Meta-Heuristic for Subset Selection Problems

Résumé

Subset selection problems involve finding an optimal feasible subset of an initial set of objects with respect to an objective function and/or some constraints. Many well-known combinatorial problems are members of this class, e.g., maximum clique problems, knapsack problems, boolean satisfiability problems, constraint satisfaction problems, and graph matching problems. In this chapter we define a generic ant colony optimization (ACO) algorithm for this class of problems. Basically, this algorithm successively generates subsets through the repeated selection of objects, and uses “pheromone trails” as a greedy heuristic to choose, at each step, the next object to be selected. The algorithm is parameterized by a pheromonal strategy and we propose and compare two different instantiations of it: a first one where pheromone is laid on objects and a second one where pheromone is laid on cliques of objects. The proposed algorithm is also parameterized by problem-specific features, and we present and evaluate instantiations of it for solving maximum clique problems, knapsack problems and constraint satisfaction problems.
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Dates et versions

hal-01541555 , version 1 (25-03-2020)

Identifiants

  • HAL Id : hal-01541555 , version 1

Citer

Christine Solnon, Derek Bridge. An Ant Colony Optimization Meta-Heuristic for Subset Selection Problems. Nadia Nedjah; Luiza de Macedo Mourelle. System Engineering using Particle Swarm Optimization, Nova Science publishers, pp.3-25, 2006, 1-60021-119-4. ⟨hal-01541555⟩
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