Introducing the Environment in Ant Colony Optimization

Abstract : Meta-heuristics are general-purpose methods for global optimization, which take generally inspiration from natural behaviors and phenomena. Among the others, Ant Colony Optimization (ACO) received particular interest in the last years. In this work, we introduce the environment in ACO, for the meta-heuristic to perform amore realistic simulation of the ants' behavior. Computational experiments on instances of the GPS Surveying Problem (GSP) show that the introduction of the environment in ACO allows us to improve the quality of obtained solutions.
Type de document :
Chapitre d'ouvrage
Studies in Computational Intelligence, 655, Springer, pp.147-158, 2016, Recent Advances in Computational Optimization
Liste complète des métadonnées

https://hal.inria.fr/hal-01402423
Contributeur : Antonio Mucherino <>
Soumis le : jeudi 24 novembre 2016 - 16:14:15
Dernière modification le : mercredi 11 avril 2018 - 01:51:06

Identifiants

  • HAL Id : hal-01402423, version 1

Citation

Antonio Mucherino, Stefka Fidanova, Maria Ganzha. Introducing the Environment in Ant Colony Optimization. Studies in Computational Intelligence, 655, Springer, pp.147-158, 2016, Recent Advances in Computational Optimization. 〈hal-01402423〉

Partager

Métriques

Consultations de la notice

340