Researchand Application Of Intelligence Algorithm For Wireless Sensor Networks

With the improvement of society and development of science and technology at full speed,the social production process in much field needs more and more accuracy and precision. Optimization design has played an increasingly important role in these field, various intelligent algorithms have emerged, and have played an irreplaceable role. Particle Swarm Optimization (PSO) algorithm is one of this kind, algorithm is simple to use, and has the widespread adaptivity and applicability in a variety of different types of engineering problems. Therefore, it has been applied effectively in many fields.Internet of Things is an important part of new generation information technology, with in-depth study of Internet of Things and it gradually promote the application of all walks of life, wireless sensor networks plays a key role in promoting. Wireless sensor network is a revolution in collection and perception of information, and has been playing an increasingly important role. Collecting data not only need accurate values, but also need precise location information. In some special applications, accurate location information of nodes is very important.The purpose of this paper is to research Quantum-behaved Particle Swarm Optimization algorithm (QPSO for short). For solving the problems of standard Harmony Search (HS) and quantum particle swarm optimization (QPSO)algorithm badly for solving high-dimensional optimization. A hybrid algorithm of harmony search and quantum particle swarm optimization algorithm is presented. In the new optimization algorithm, the best individual produced in each generation of QPSO evolution process into the harmony memory with the metabolic manner and using the advantages of Harmony Search algorithm's strong local search ability. Moreover, an adjustable parameter is regulated and new element is added during the iteration process to maintain the diversity of the whole swarm. Therefore the algorithm can avoid falling into local optimal solution and increase the global search ability. Simulation tests of five typical functions shows that the proposed algorithm can efficiently improve accuracy of converge. And demonstrates that efficiency and rationality of the improved algorithm for solving high-dimensional and complex global optimization problem.For the issue of that accuracy is not high and energy consumption is large in wireless sensor network node-positioning, then a new algorithm for optimizing the localization of nodes is presented, which is based on the hybrid optimization algorithm of QPSO and HS model, and the final exact location of the nodes can be obtained by the optimization results of the proposed algorithm. In the node localization process, algorithm can improve the positioning accuracy, fast Converge to the optimal solution. Under the same circumstances, it can reduce the energy consumed in the process, can improve efficiency to some extent. Experiments proved it to be a very effective method.