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Conference Papers Year : 2006

Ant Colony Optimization for Multivalent Graph Matching Problems

Abstract

The Ant Colony Optimization (ACO) meta-heuristic is a bio-inspired approach where a colony of artificial ants cooperates via pheromone trails in order to solve a combinatorial optimization problem. Intuitively, a key point when developing any ACO algorithm is to define the pheromonal strategy, i.e., to decide where pheromone trails should be laid and how they should be exploited and updated during the solution process. In this paper, we study the influence of considering different pheromonal strategies on the ant search capabilities when solving multivalent graph matching problems. Two different ACO algorithms, with respect to two different pheromonal strategies, are proposed and experimentally compared.
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Dates and versions

hal-01541565 , version 1 (19-06-2017)

Identifiers

  • HAL Id : hal-01541565 , version 1

Cite

Olfa Sammoud, Christine Solnon, Khaled Ghedira. Ant Colony Optimization for Multivalent Graph Matching Problems. ECAI Workshop on Evolutionary Computation ((EC)2AI 2006), Aug 2006, Trentinon, Italie, Italy. ⟨hal-01541565⟩
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