Ant Colony Optimization with Environment Changes: an Application to GPS Surveying

Abstract : We propose a variant on the well-known Ant Colony Optimization (ACO) general framework where we introduce the environment to play an important role during the optimization process. Together with diversification and intensification, the environment is introduced with the aim of avoiding the search to get stuck at local optima. In this work, the environment is simulated by means of the Logistic map, that is used in ACO for perturbing the update of the pheromone trails. Our preliminary experiments show that our environmental ACO (eACO), with variable environment, outperforms the standard ACO on a set of instances of the GPS Surveying Problem (GSP).
Type de document :
Communication dans un congrès
Federated Conference on Computer Science and Iinformation Ssystems, Sep 2015, Lodz, Poland. 2015, IEEE conference proceedings
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https://hal.inria.fr/hal-01196694
Contributeur : Antonio Mucherino <>
Soumis le : jeudi 10 septembre 2015 - 11:48:15
Dernière modification le : mercredi 16 mai 2018 - 11:23:35

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  • HAL Id : hal-01196694, version 1

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Antonio Mucherino, Stefka Fidanova, Maria Ganzha. Ant Colony Optimization with Environment Changes: an Application to GPS Surveying. Federated Conference on Computer Science and Iinformation Ssystems, Sep 2015, Lodz, Poland. 2015, IEEE conference proceedings. 〈hal-01196694〉

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