A dynamic state transition algorithm with application to sensor network localization

Abstract

The sensor network localization (SNL) problem aims to reconstruct the positions of all the sensors in a network with given distance between pairs of sensors and within the radio range between them. It is proved that the computational complexity of the SNL problem is NP-hard, and semi-definite programming or second-order cone programming relaxation methods can only solve some special problems of this kind. In this study, a stochastic intelligent optimization method based on the state transition algorithm is introduced to solve the SNL problem without additional assumptions and conditions on the problem structure. To transcend local optimality, a novel dynamic adjustment strategy called “risk and restoration in probability”is incorporated into the state transition algorithm. An empirical study is investigated to appropriately choose the risk probability and restoration probability, yielding the dynamic state transition algorithm, which is further improved with gradient-based refinement. The refined dynamic state transition algorithm is applied to the SNL problem, and satisfactory simulation results show the effectiveness of the proposed approach.