Business rules management improvement through the application of Particle Swarm Optimization algorithm and Artificial Neural Networks

Well-timed and adequate reaction to the market changes is the core issue for each company, which goal is to be competitive and reduce the risk. In order to stay in the market company has to be able to analyze market situation and make right decisions. The main goal of the paper is to introduce the decision making model which could bridge the gap between business rule management and artificial intelligence approaches. The proposed model is based on the application of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) algorithm. In the proposed decision making model ANN are applied in order to make the analysis of data and to calculate the decision. Subsequently, the application of SPO algorithm is made. The core idea of this algorithm application is to select the "global best" ANN for decision making and to adapt the weight of other ANN towards the weights of the bets network. The case analysis presented in this paper show the potentiality of PSO algorithm applications for the decision making.