In this paper we present a comparative study of four trajectory or single-solution based metaheuristics (S-metaheuristics): Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP), Variable Neighborhood Search (VNS), and Simulated Annealing (SA). These metaheuristics were considered to assess their respective performance to minimize the Maximum Tardiness (Tmax) for the ...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

ABSTRACT Multiagent learning literature has looked at iterated twoplayer games to develop me hanisms that allow agents to learn to onverge on Nash Equilibrium strategy pro les. Su h equilibrium on guration implies that there is no motivation for one player to hange its strategy if the other does not. Often, in general sum games, a higher payo an be obtained by both players if one hooses not to ...

The purpose of this paper is to provide a preliminary report on the rst broad-based experimental comparison of modern heuristics for the asymmetric traveling salesmen problem (ATSP). There are currently three general classes of such heuristics: classical tour construction heuristics such as Nearest Neighbor and the Greedy algorithm, local search algorithms based on rearranging segments of the t...

The purpose of this paper is to provide a preliminary report on the rst broad-based experimental comparison of modern heuristics for the asymmetric traveling salesmen problem (ATSP). There are currently three general classes of such heuristics: classical tour construction heuristics such as Nearest Neighbor and the Greedy algorithm, local search algorithms based on rearranging segments of the t...

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for responsedependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in the general setting. We show that a different property of the cost function co...

Recommendation system is able to shape user demands, which can be used for boosting caching gain. In this paper, we jointly optimize content caching and recommendation at base stations. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific threshold. We then formulate a joint caching and recommendation problem maximizing the cac...