Abstract
This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm (GA) are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory (MATLAB) platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution.
Highlights
With the development of computer information technology and artificial intelligence (AI) in recent years, a series of new methods and new technologies have emerged, among which the artificial neural network (ANN) is a typical representative [1, 2]
Different from the conventional neural network (CNN) model, the backpropagation neural network (BPNN) model is a multilayer forward neural network that can realize the random nonlinear mapping of corresponding input and output as well as the autonomous learning, so it has emerged in the processing and solution of nonlinear issues [3, 4]. e nonlinear issues are closely related to the actual production and life of human beings, such as vehicle fuel consumption and construction prediction. e genetic algorithm (GA) is a global optimization algorithm, taking code as the calculation object and search information as the objective function, respectively. e selection operation, crossover operation, and mutation operation related to the algorithm are all expanded in the form of probability, so the search process is very flexible [5]
Ling et al applied the optimized GA-BPNN algorithm to the prediction and assessment of accident consequences; after comparison on the simulation results obtained by the BPNN algorithm and the GA-BPNN algorithm, they found that the latter cannot optimize the existence of BPNN with the improved calculation speed and accuracy [36]
Summary
Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm. Received 29 April 2021; Revised May 2021; Accepted June 2021; Published 6 July 2021. Is study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. E results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. E average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. E output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. The OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution
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