Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method base...Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed.First,the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity,and a truncation probability sensing model is presented.Two strategies,the local mutation operation and parameter adaptive operation,are introduced to improve the optimization ability of quantum particle swarm optimization(QPSO)algorithm,and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front.Then,considering the multi-objective deployment requirements,a novel multi-sensor deployment method based on the multi-objective optimization theory is built.Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once.Compared with the traditional algorithms,the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.展开更多
基金This work is also supported by the National Defence Advance Research of China[No.012015012600A2203].
文摘Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed.First,the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity,and a truncation probability sensing model is presented.Two strategies,the local mutation operation and parameter adaptive operation,are introduced to improve the optimization ability of quantum particle swarm optimization(QPSO)algorithm,and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front.Then,considering the multi-objective deployment requirements,a novel multi-sensor deployment method based on the multi-objective optimization theory is built.Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once.Compared with the traditional algorithms,the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.
