, nous avons présenté un algorithme hybride entre la métaheuristique d'optimisation par colonies de fourmis et la programmation par contraintes pour la résolution de problèmes de satisfaction de contraintes
, nous allons présenter une nouvelle approche hybride et générique dans laquelle ACO est combiné avec l'approche B&P&B pour résoudre des problèmes d'optimisation sous
, Vertex sont meilleure en moyenne que CPO-ACO-Vertex-ph1 sur toutes les instances utilisées Cela est du au fait que CPO-ACO-Vertex-ph1 n'effectue pas de phase d'intensification de la recherche (il n'utilise pas la deuxième phase) Ce tableau montre également que CPO-ACO-Vertex est meilleure en moyenne que CPO-ACO sur sept instances et il est moins bon que lui sur trois instances. Ces résultats montrent, en comparant CPO-ACO avec CPO-ACO-Vertex, l'intérêt de la nouvelle structure de phéromone Vertex. La comparaison de CPO-ACO-Vertex-ph1 et CPO-ACO-Vertex montre l'intérêt de la deuxième phase (la phase d'intensification) en utilisant CP Optimizer, Analyse des résultats Le tableau 6.2 montre sur Remarquons que sur les pour le MKP, CPO-ACO et CPO-ACO-Vertex trouvent les solutions optimales
, test de Student avec un niveau de confiance de 95%) de comparaison des quatre algorithmes en se basant sur les résultats donnés dans le tableau 6.2. Pour chaque paire d'algorithmes, nous donnons le pourcentage d'instances sur lesquelles l'algorithme qui est sur la ligne du tableau est meilleur que l'algorithme sur la colonne, donne les résultats des tests statistiques Nous remarquons que CPO ? ACO ? Vertex est
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