A Distributed Hybrid Taboo Search Localization Algorithm

Centralized positioning communication overhead is big and poor scalability, wireless sensor network that is resource-constrained and self-organized has some limitations. Through the positioning problem is transformed into unconstrained optimization problem, this paper proposes a distributed localization algorithm based on hybrid taboo search. To reduce the influence of the ranging error and obtain better neighborhood solution generated interval, designed a selection operator, select the appropriate beacon nodes to participate in orientation. Then, use of a beacon node information to obtain the initial solution. Finally, using the combined hybrid strategy of taboo search and simulated annealing to location optimization, further improve the positioning accuracy. In essence it is a kind of two phase refinement localization algorithm. Simulation shows that this algorithm can gain higher positioning accuracy under different noise factors and beacon nodes.