Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

Qu, Shaojian

doi:10.1155/2021/1718234

Cited by 22 publications

(16 citation statements)

References 37 publications

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“…However, it also has some disadvantages such as low convergence speed and instability [15]. In order to solve the limitation BPNN the author in [16] proposed to combine it with the Genetic Algorithm (GA) to improve its structure. However, in GA, chromosomes exchange information with each other through crossover, which is a two-way information sharing mechanism with long loop times.…”

Section: The Load Shedding Control Strategymentioning

confidence: 99%

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

Nhung

Phung²,

et al. 2022

Eng. Technol. Appl. Sci. Res.

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This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.

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Section: The Load Shedding Control Strategymentioning

confidence: 99%

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

Nhung

Phung²,

et al. 2022

Eng. Technol. Appl. Sci. Res.

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show abstract

“…It helps to avoid premature convergence and getting stuck in local optima problems of MLP. In [ 43 ], an optimized adaptive GA in the backpropagation neural network (OAGA-BPNN) is proposed to optimize BPNN for traffic flow prediction. In [ 44 ], a hybrid grasshopper and new cat swarm optimization algorithm was proposed for feature selection and weight and architecture optimization of MLP.…”

Section: Related Workmentioning

confidence: 99%

GGA‐MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Bansal

Lamba

Jain

et al. 2022

Contrast Media & Molecular Imaging

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The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques.

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“…We have analyzed the crossover rate and the mutation rate of standard GA in Zhang & Qu (2021) . And we have obtained the result that a too large crossover rate or a too small crossover rate will affect the search efficiency of the GA. An optimized GA has been proposed in Jiao et al (2019) , in which the mutation rate and crossover rate can be adjusted appropriately.…”

Section: The Optimization Of Genetic Algorithmmentioning

confidence: 99%

“…And we have obtained the result that a too large crossover rate or a too small crossover rate will affect the search efficiency of the GA. An optimized GA has been proposed in Jiao et al (2019) , in which the mutation rate and crossover rate can be adjusted appropriately. And also we introduced the variable crossover process and mutation rate parameters in Zhang & Qu (2021) , The adaptive crossover rate P C based on the IGA can be expressed as Eq. (5) , as follows:…”

Section: The Optimization Of Genetic Algorithmmentioning

confidence: 99%

Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction

Zhang¹,

Qu²,

Zhang³

et al. 2022

PeerJ Computer Science

Self Cite

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Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy.

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Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

Cited by 22 publications

References 37 publications

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

GGA‐MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction

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