A Multi-Objective Decision-Making Neural Network: Effective Structure and Learning Method

Abstract

Decision Neural Networks significantly improve the performance of complex models and create more transparent and accountable decision-making systems that can be trusted in critical applications. However, their performance strongly depends on the amount of data and the learning algorithm. This article describes the development of a simplified structure and training algorithm based on the Levenberg–Marquardt algorithm to enhance the decision neural network's training and assess the utility function's efficacy in multi-objective issues. The suggested algorithm converges faster than traditional algorithms. Also, the designed scheme combines gradient descent with the Gauss-Newton method, allowing it to escape shallow local minima more effectively than other similar techniques. Numerical examples demonstrate how well the suggested method estimates linear utility functions, even complicated and nonlinear ones. Additionally, the findings of applying the enhanced decision neural network to multi-objective decision-making issues show that this instructional technique produces responses with higher quality and faster convergence. By applying the designed scheme to a multi-objective problem with seven primary answers, it is shown that accuracy is improved by more than 20%.