Optimal Design Of Ocean Observation Scheme Based On Improved Particle Swarm Algorithm

The optimal observation scheme design of regional marine mobile observation network is a sampling path planning of autonomous underwater vehicle considering the space-time information of marine environment and observation constraints of mobile observation platform.This scheme provides a high-efficiency and low-cost data supplement method for data assimilation and marine environmental forecasting of sub-mesoscale oceans.However,in the process of solving the scheme based on the traditional heuristic optimization algorithm,there are problems that the optimization algorithm is insufficient in the optimization ability and easy to fall into the local minimum solution.The planned observation path of the marine mobile observation platform deviates from the optimal,which greatly reduces the effectiveness of observation sampling.Based on this,on the one hand,this paper solves the problem that the algorithm falls into a local minimum solution by using the dynamic exponentially decreasing inertia weight of random oscillation and a hybrid mutation mechanism;on the other hand,this paper introduces an improved strategy to enhance optimal particle guidance.A comparison experiment of benchmark function is designed to verify the effectiveness of the improved algorithm.The improved algorithm is applied to the optimization design of the optimal observation scheme of the regional ocean mobile observation network.The experimental results show that the improved particle swarm algorithm can effectively break through the constraints of the local optimal solution,has a stronger global optimal solution search ability,and can obtain better sampling in the optimal observation path planning problem of the regional ocean mobile observation network.path to improve the observation efficiency of the regional ocean observation network and the analysis and forecasting capabilities of marine environmental elements.The main research contents of this paper are as follows:Firstly,in view of the problem that the traditional particle swarm algorithm falls into the local minimum solution,this paper proposes a stochastic oscillation dynamic exponentially decreasing inertia weight mechanism and a hybrid mutation mechanism,which enrich the particle swarm from the perspectives of population behavior and particle motion respectively.At the same time,combined with the advantages of the two optimization strategies,an improved particle optimization algorithm with a hybrid optimization strategy is proposed,and the effectiveness of the optimization strategy is verified by simulation experiments with benchmark function design.The experimental results show that the particle swarm optimization algorithm based on the hybrid optimization strategy can strengthen the local search ability,increase the diversity of the population,improve the escape ability of the local optimal solution,and can effectively solve the problem of falling into the local minimum solution.Then,in view of the problem of insufficient particle swarm optimization ability,this paper designs an improved strategy to enhance the guidance of global optimal particles.Mixed strategy particle swarm algorithm,and designed a simulation comparison and verification experiment based on the benchmark function.The experimental results show that after introducing the enhanced global optimal particle strategy,the algorithm has achieved better results in terms of convergence accuracy and convergence speed.It shows that the algorithm achieves the improvement of the optimization ability of the algorithm on the basis of effectively breaking through the local minimum solution.Finally,in order to solve the problem that the optimization of the traditional regional ocean mobile observation scheme design method takes too long and the calculation cost is too high,this paper proposes a pre-interpolation method of ocean information based on the background field,and the optimization calculation speed is increased by 4 times.On this basis,this paper applies the improved particle swarm algorithm to the optimal observation scheme design of the regional ocean mobile observation network,and combines the traditional particle swarm algorithm to design a variety of network design schemes for comparison.The experimental results show that,compared with the traditional particle swarm optimization to solve the optimization scheme design problem of the ocean mobile observation network,the improved particle swarm optimization can effectively break through the constraints of the local optimal solution,has a stronger global optimal solution search ability,and can effectively overcome the constraints of the local optimal solution.Plan a better observation path for the regional ocean mobile observation platform to improve the observation efficiency of the regional ocean observation network and the ability to analyze and forecast marine environmental elements.