Adel Ammar

Although there exist efficient methods to determine an optimal path in a graph, such as Dijkstra and A* algorithms, large instances of the path planning problem need more adequate and efficient techniques to obtain solutions in reasonable time. We propose two new time-linear relaxed versions of Dijkstra (RD) and A* (RA*) algorithms to solve the global path planning problem in large grid environments. The core idea consists in exploiting the grid-map structure to establish an accurate approximation of the optimal path, without visiting any cell more than once. We conducted extensive simulations (1290 runs on 43 maps of various types) for the proposed algorithms, both in four-neighbor and eight-neighbor grid environments, and compared them against original Dijkstra and A* algorithms with different heuristics. We demonstrate that our relaxed versions exhibit a substantial gain in terms of computational time (more than 3 times faster in average), and in most of tested problems an optimal solution (in at least 97 % of cases for RD and 82 % for RA*) or a very close one is reached (at most 9 % of extra length, and less than 2 % in average). Besides, the simulations also show that RA* provides a better trade-off between solution quality and execution time than previous bounded relaxations of A* that exist in the literature.

ABSTRACT With the emergence of High Speed Network (HSN), the manual intrusion alert detection become an extremely laborious and time-consuming task since it requires an experienced skilled staff in security fields and need a deep analysis. In addition, the batch model of alert management is no longer adequate given that labeling is a continuous time process since incoming intrusion alerts are often collected continuously in time. Furthermore, the static model is no longer appropriate due to the fluctuation nature of the number of alerts incurred by Internet traffic fluctuation nature. This paper proposes an efficient real time adaptive intrusion detection alert classifier dedicated for high speed network. Our classifier is based an online self-trained SVM algorithm with several learning strategies and execution modes. We evaluate our classifier against three different data-sets and the performance study shows an excellent results in term of accuracy and efficiency. The predictive local learning strategy presents a good tradeoff between accuracy and time processing. In addition, it does not involve a human intervention which make it an excellent solution that satisfy high speed network alert management challenges.

ABSTRACT This paper investigates the capabilities of tabu search for solving the global path planning problem in grid maps. Accordingly, a tabu search system model is designed and a tabu search planner algorithm for solving the path planning problem is proposed. A comprehensive simulation study is conducted using the proposed model and algorithm, in terms of solution quality and execution time. A comparison between our results with those of A* and genetic algorithms (GA) is presented for small, medium and large-scale grid maps. Simulation results show that the tabu search planner is able to find the optimal solution for small scale environments. However, for large scale maps, it provides near-optimal solutions with small gap while ensuring shorter execution times as compared to the A* Algorithm. A discussion about the advantages and limitations of TS for solving a path planning problem is also presented.

ABSTRACT Global path planning is considered as a fundamental problem for mobile robots. In this paper, we investigate the capabilities of genetic algorithms (GA) for solving the global path planning problem in large-scale grid maps. First, we propose a GA approach for efficiently finding an (or near) optimal path in the grid map. We carefully designed GA operators to optimize the search process. We also conduct a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality, and we compare it against the well-known A* algorithm as a reference. Extensive simulation results show that GA is able to find the optimal paths in large environments equally to A* in almost all the simulated cases.

Abstract This paper studies the evolution of high performance computing (HPC) and its trends. It exposes the different architectures used in HPC, the common high-speed networks, the programming models, the communications models, and the communication libraries. ...

... Akhlaq, M., Alserhani, F., Subhan, A., Awan, IU, Mellor, J., & Mirchandani, P. (2010). High speed NIDS using dynamic cluster and comparator logic. In Proceedings of the IEEE 10th International Con-ference on Computer and Information Technology (pp. 575-81). Ammar, A., & ...

ABSTRACT This paper investigates the capabilities of tabu search for solving the global path planning problem in grid maps. Accordingly, a tabu search system model is designed and a tabu search planner algorithm for solving the path planning problem is proposed. A comprehensive simulation study is conducted using the proposed model and algorithm, in terms of solution quality and execution time. A comparison between our results with those of A* and genetic algorithms (GA) is presented for small, medium and large-scale grid maps. Simulation results show that the tabu search planner is able to find the optimal solution for small scale environments. However, for large scale maps, it provides near-optimal solutions with small gap while ensuring shorter execution times as compared to the A* Algorithm. A discussion about the advantages and limitations of TS for solving a path planning problem is also presented.