Multi-objective Gray Wolf Optimization Research On Improved Vehicle Positioning And Layout In Complex Traffic Tunnels

The positioning of water conservancy project dam material transportation vehicles in the tunnel is vital to the intelligent management of dam construction.The application of wireless sensing technology in tunnels lacking positioning signals such as Bei Dou can effectively achieve vehicle positioning.However,the current positioning research is mostly based on artificial experience for the deployment of sensing nodes.It lacks comprehensive consideration of node sensing coverage,node communication loss and deployment cost indicators,and it is difficult to adapt to complex tunnel environments.Although the classic gray wolf algorithm has more advantages in solving complex optimization problems.Strong self-adaptation,but for the engineering optimization problems in this paper,there are disadvantages such as low convergence efficiency and easy to fall into local optimum.Therefore,this paper has carried out a multi-objective gray wolf optimization study on the improvement of vehicle positioning in complex traffic tunnels.The main research results are as follows:(1)Aiming at the problems that the existing wireless positioning technology is difficult to adapt to the complex application environment and the node layout is mostly based on manual experience and lack of scientific,a Zig Bee-based complex tunnel positioning node layout model is proposed.Through the analysis of the environmental characteristics of the Zig Bee wireless positioning technology,such as the multipath effect and the closedness and restriction of complex caverns,comprehensively considering the impact of the two on the deployment of wireless positioning nodes,based on the multi-objective optimization technology,the construction of the complex tunnel vehicle positioning and deployment model of node communication energy loss and node deployment cost targets provides a scientific theoretical basis for achieving stable and efficient services of Zig Bee wireless positioning network in complex application environments.(2)In order to solve the problem of the classic gray wolf algorithm in solving the engineering optimization NP hard problem,it is easy to fall into the local optimum and the convergence efficiency is low,an intelligent calculation optimization method based on the improved multi-objective gray wolf algorithm is proposed.Considering the improved multi-objective grey wolf optimizer(IMOGWO)based on chaotic mapping theory,nonlinear control parameter strategy and l(?)vy flight algorithm,it has the characteristics of high convergence efficiency and strong global optimization ability.The above-mentioned IMOGWO is adopted.Methods Solving the optimization model of sensory node placement can make up for the traditional multiobjective gray wolf algorithm that is easy to fall into local optimality and poor stability in the optimization process,so as to obtain the Pareto optimal solution set and realize the optimal placement of positioning nodes.(3)Taking an actual project in Southwest China as an example,the abovementioned model and method were used to carry out engineering application research on the perception and positioning of dam material transportation vehicles in complex tunnels.Applying the positioning node optimization model and optimization method constructed in this paper to engineering practice,the results show that compared with the traditional artificial solution,the optimization scheme in this paper optimizes the perception coverage and communication loss by 12.22% and 26.98%,respectively;compared with other types Optimization algorithm The perceived quality of the method in this paper is improved by 13.96%,and the deployment cost is reduced by 6%.In addition,the perceived data packet loss rate is low,and the system runs stably,which proves the superiority and feasibility of the model and method in this paper.