Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Modèle théorique et outil de simulation pour une meilleure évaluation des claviers logiciels augmentés d'un système de prédiction de mots

Les claviers logiciels se sont démocratisés pour rendre possible la saisie de textes en mobilité sur des dispositifs dépourvus de claviers physiques tels que les téléphones portables nouvelle génération. Cependant, ces claviers présentent plusieurs inconvénients comme la lenteur de la saisie et la fatigue engendrées pour les utilisateurs déficients moteurs. La solution intuitive était d'allier ces logiciels à des listes contenant les mots susceptibles de continuer la saisie d'un mot initié par l'utilisateur. Bien que ces listes, dites listes de prédiction, réduisent le nombre de clics et le nombre d'opérations, la vitesse de saisie de l'utilisateur a diminué. Une expérimentation outillée d'un système de suivi du regard a ainsi permis de déterminer des " stratégies " de fonctionnement de l'utilisateur face à une liste de mots. Ces résultats ont ainsi permis d'affiner les modèles de prédiction de manière à réduire l'écart séparant les performances prédites des performances réellement enregistrées. A partir des constats effectués lors de la première expérimentation, nous proposons deux variantes de l'utilisation des listes de prédiction de mots. La première propose un nouveau moyen d'interagir avec la liste de mots et permet ainsi de maximiser l'utilisation de celle-ci. La seconde évalue un repositionnement de la liste de mots de manière à réduire le nombre de mouvements oculaires vers la liste. Ces deux évolutions, évaluées théoriquement puis au moyen d'une expérimentation utilisateur, permettent ainsi d'améliorer les performances de saisie par rapport à une liste de prédiction de mots classique.Predictive model and simulation tool for a best evaluation of soft keyboard augmented by words prediction list The software keyboards are used to enable text input in mobility and for devices without physical keyboards, such as the new generation of mobile phones. However, these keyboards have several drawbacks such as slowness text entry and fatigue generated for motor impaired users. The solution was to combine software keyboard to lists containing the words likely to continue the word introduced by the user. While these lists, so-called prediction lists, reduce the number of clicks and the number of operations, the speed of user input has decreased. An experiment with an eye tracking system has identified the "strategies" of the user while using and searching a list of words. These results were helpful to refine the prediction models in order to reduce the gap between the performance predicted and the performance actually recorded. Based on observations made during the first experiment, we propose two variants of the use of word prediction list. The first proposes a new way to interact with the list of words and allows maximum use of it. The second evaluates a repositioning of the list of words in order to reduce the number of eye movements to the list. These two propositions were theoretically and experimentally evaluated by users. These software can improve the input performances compared with a classic word prediction list