. Gómez-villouta, et évalue des placements, éventuellement partiels, en utilisant plusieurs informations du problème. Finalement, notre algorithme inclut un mécanisme de diversification basé sur l'historique de la recherche. Les résultats expérimentaux montrent que CTS présente un certain intérêt pour résoudre le SPP. La recherche tabou (RT) est l'une des méta-heuristiques les plus utilisées pour résoudre les problèmes d'optimisation [Glover and Laguna Elle explore un voisinage en utilisant une mémoire. Soient (S, f ) l'espace de recherche et la fonction d'évaluation respectivement. Un voisinage N (pour " neighborhood " ) sur S est une fonction qui associe à chaque individu s ? S d'autres solutions N (s) ? S. Toute solution s ? ? N (s) est un voisin de s. Pour un voisinage N , une solution s est un " optimum local " si s est la meilleure solution entre les solutions (voisins) dans N (s). La transition d'une solution à un voisin est appelée " mouvement, Chapitre 4. Un nouvel algorithme de recherche tabou pour le SPP 4.1 Introduction Dans ce chapitre, nous présentons CTS Comparé aux autres algorithmes pour le SPP, notre approche possède quelques caractéristiques particulières : CTS utilise la notion de voisinage consistant On applique un mouvement µ à une solution s en la changeant légèrement pour visiter une solution voisine s ?, 1997.

?. Cette-opération-est-dénotée-s-?-=-s, Soit ?(s) l'ensemble de tous les mouvements que l'on peut appliquer à s : N (s)={s ? µ/µ ? ?(s)}. Un algorithme de RT typique commence avec une solution initiale s ? S et, dans un processus itératif, visite d'autres solutions. À chaque itération, on cherche l'un des meilleurs voisins s ? ? N (s) pour remplacer la solution courante s, même si s ? n'améliore pas la valeur de la fonction d'évaluation de la solution actuelle. Finalement, pour tenter d'éviter les cycles et permettre une exploration plus efficace, l'algorithme utilise une mémoire à court terme appelée

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