Development of a Genetic Algorithm-based Decision Neural Network for the Preference Assessment in Multi-objective Decision-Making Problems

The application of neural networks in estimating and describing the structure of decision makers' priorities has been very much considered in solving multi-objective decision problems in recent years. The neural network is a new approach to estimate the decision-making function of a multi-objective problem. Developing and improving the teaching methods of these types of networks facilitate to find the preferred solution in multi-dimensional issues, especially large-scale issues. In this paper, the educational method is developed to increase the efficiency of a neural network. In addition, a genetic algorithm is used to train this neural network. Furthermore, an improved cost function is proposed to adjust the parameters of the neural network and based on this function the cost parameters of the neural network are optimized. The efficiency of the proposed method is shown in solving several practical examples, including linear/nonlinear and discrete/continuous optimization problems. The efficiency of the proposed method is shown in solving several practical examples, including linear/nonlinear and discrete/continuous optimization problems.