Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

TL;DR

This paper introduces a hybrid neural network training method combining artificial bee colony algorithms with backpropagation to enhance convergence speed and optimization quality, compared to genetic algorithm-based methods.

Contribution

It presents an improved artificial bee colony algorithm integrated with backpropagation for faster, more efficient neural network training, a novel hybrid approach in this domain.

Findings

Faster convergence rate compared to genetic algorithm-based methods.

Improved optimization outcomes on standard datasets.

Enhanced training efficiency of feed-forward neural networks.

Abstract

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.