For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode.
Abstract. Topology control is the problem of assigning powers to the nodes of an ad hoc network so as to create a specified network topology while minimizing the energy consumed by the network nodes. Topology control in mobile wireless ad hoc networks under the Simple Mobile Network (SMN) Model was introduced in [6]. In that model, there is only one moving node. That node moves on a straight line segment throughout a unit time interval and no assumptions are made about the moving speed. In this paper we study the problem of minimizing the maximum power consumed by any network node to maintain a 1-Connected (i.e. connected) topology under the SMN model. For that problem, in [6], a decision algorithm that runs in time O(n 2 ) and an optimization algorithm that runs in time O(n 2 log n) are described. In this paper we improve upon those results by taking advantage of Voronoi diagrams and Delaunay triangulations. We provide three main results: a decision algorithm that runs in time O(n log n); an optimization algorithm with an expected running time of O(n 7/4 log n); and a constant factor approximation algorithm that runs in time O(n log n). Simulation results evaluating the approximation algorithm are also provided.
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