Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
This work addresses convergence issues in neural network training for researchers, but it is incremental as it builds on existing hybrid optimization techniques.
The paper tackles the problem of slow convergence in feed-forward neural network training by proposing a hybrid method combining artificial bee colony optimization with backpropagation, resulting in improved convergence speed compared to a genetic algorithm-based backpropagation method on standard datasets.
Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.