Khaled Ghédira

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We propose in this paper a generic algorithm based on Ant Colony Optimization to solve multi-objective optimization problems. The proposed algorithm is parameterized by the number of ant colonies and the number of pheromone trails. We compare different variants of this algorithm on the multi-objective knapsack problem. We compare also the obtained results(More)
Evolutionary algorithms have amply demonstrated their effectiveness and efficiency in approximating the Pareto front of different multi-objective optimization problems. Fewer attentions have been paid to search for the preferred parts of the Pareto front according to the decision maker preferences. Knee regions are special portions of the Pareto front(More)
We introduce in this paper a new multi-objective memetic algorithm. This algorithm is a result of hybridization of the NSGA-II algorithm with a new designed local search procedure that we named Pareto Hill Climbing. Verification of our novel algorithm is carried out by testing it on two sets of multi-objective test problems and comparing it to other(More)
In this paper, we provide a detailed overview of existing researches in the field of software restructuring and model refactoring, from a formal as well as a practical point of view. We propose a possible taxonomy for the classification of several existing and proposed model refactoring approaches. The taxonomy is described with a feature model that makes(More)