scholarly journals An Improved Differential Evolution Algorithm and Its Application to Large-Scale Artificial Neural Networks

2017 ◽  
Vol 806 ◽  
pp. 012010 ◽  
Author(s):  
Tae Jong Choi ◽  
Chang Wook Ahn
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm ◽  
Artificial Neural ◽  
Improved Differential Evolution Algorithm

scholarly journals The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting

2016 ◽  
Vol 6 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Eren Bas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Neuron Model ◽  
Differential Evolution Algorithm ◽  
Series Data ◽  
Feed Forward ◽  
Evolution Algorithm ◽  
Artificial Neural

Abstract In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Goal Programming Model ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

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