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

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.Comment: 14 Pages, 11 figure

Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

The optimization algorithms that imitate nature have acquired much attention principally mechanisms for solving the difficult issues for example the travelling salesman problem (TSP) which is containing routing and scheduling of the tasks. This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. Bees Algorithm is one of the optimization algorithms inspired from the natural foraging ways of the honey bees of finding the best solution. It is a series of activities based on the searching algorithm in order to access the best solutions. It is an iteration algorithm; therefore, it is suffering from slow convergence. The other downside of the Bee Algorithm is that it has needless computation. This means that it spends a long time for the bees algorithm converge the optimum solution. In this study, the parallel bees algorithm technique is proposed for overcoming of this issue. Due to that, this would lead to reduce the required time to get a solution with faster results accuracy than original Bees Algorithm

Computational Intelligence Modeling of Pharmaceutical Properties

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.In the pharmaceutical industry, a good understanding of the casual relationship between product quality and attributes of formulations is very useful in developing new products, and optimizing manufacturing processes. Feature selection is mandatory due to the abundance of noisy, irrelevant, or misleading features. The selected features will improve the performance of the prediction model and will provide a faster and more cost effective prediction than using all the features. With the big data captured in the pharmaceutical product development practice, computational intelligence (CI) models and machine learning algorithms could potentially be used to identify the process parameters of formulations and manufacturing processes. That needs a deep investigation of roller compaction process parameters of pharmaceutical formulations that affect the ribbons production. In this work, we are using the bio-inspired optimization algorithms for feature selection such as (grey wolf, Bat, flower pollination, social spider, antlion, moth-flame, genetic algorithms, and particle swarm) to predict the different pharmaceutical properties.European Cooperation in Science and Technology. COSTThis work was supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 316555. In addition, this work was partially supported by NESUS