At present, vehicle routing optimization has become the key to improving logistics efficiency and reducing costs. This article proposes an improved ant colony algorithm to address the limitations of traditional ant colony algorithms in the optimal path problem for vehicles. The core of this study is to improve the pheromone update model of ant colony algorithm and validate it by constructing an experimental environment. The improved ant colony algorithm proposed in this article has significant performance improvements in solving vehicle path optimization problems, and is feasible and superior in practical applications, especially in terms of search efficiency. This algorithm provides a new perspective for future research directions.
Traffic flow prediction has a good guiding effect on traffic control. In response to the current inability of road traffic flow prediction methods to fully reveal the inherent laws of traffic flow, and considering the issue of fully considering spatiotemporal correlation in traffic flow prediction, this paper proposes an LSTM (Long Short-Term Memory) model based on Bayesian optimization. Experimental studies have shown that the LSTM model based on Bayesian optimization has good performance and high prediction accuracy.
KEYWORDS: Education and training, Data modeling, Neural networks, Statistical analysis, Analytical research, Roads, Data hiding, Transportation, Signal filtering, Tunable filters
On the basis of current research on traffic flow prediction, the article proposes a traffic flow prediction method based on the fusion of K-Means algorithm and GRU. This method first uses K-means for clustering analysis of traffic flow and establishes a traffic flow pattern database, and then predicts traffic flow through GRU training. After simulation experiments, the MAPE and RMSE values of the traffic flow prediction method based on the fusion of K-Means and GRU are lower than those of traditional GRU, LSTM, KNN, SAES, and SVM, and the fitting effect is good. It is a reference traffic prediction method.
To improve capitation accuracy by residual network and multiscale image training, there are two methods to detect caphead characteristics. Previously based on pre-described head characteristics, and methods based on the statistical training of the models, the latter is very robust, using a neural network to train, a better effect can be obtained. Based on the presence of overlapping heads in dense multi-human images and head features with varying near and far scales, this requires more special treatment for the training sample of character avatar. For problems that cannot be detected in real time, using an alternative method of YOLO V2 detection can remedy the defects and help obtain the relevant application model.
In this paper, ant colony algorithm and genetic algorithm are combined to improve the stability and efficiency of cloud computing resource scheduling. The strategy of this algorithm is to use the local optimal solution of genetic algorithm as the initial pheromone of ant colony algorithm, and introduce load balancing adjustment factor and transfer probability into ant colony algorithm. The experimental results show that the genetic ant colony fusion algorithm in this paper has better advantages in the number of iterations, time cost, power cost and load balancing, and achieves the goal of uniform distribution of cloud computing resources.
In this paper, an improved ant colony algorithm is proposed to solve the transportation optimal path problem. The algorithm can quickly find the optimal path by improving heuristic function, pheromone and neighbourhood-search. Experimental simulation shows that the improved ant colony algorithm proposed in this paper has certain advantages over genetic algorithm and ant colony algorithm. It not only obtains the minimum value of the optimal path length, but also has the fastest convergence speed, which effectively solves the problem of optimal transportation path planning. The improved algorithm effectively accelerates the transportation speed, shortens the transportation time and saves the transportation cost.
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