An interactive multi-objective gradient search

Abstract

In recent years there has been much interest in developing interactive algorithms for solving multiple objective optimization problems. In this paper we present an approach for estimating the gradient within a line-search algorithm. The approach uses a numerical approximation to the gradient obtained by fitting a first-order experimental design to the decision maker's value function. Test results indicate that the algorithm provides accurate utility predictions for numerically assessed gradient vectors, relative to analytical gradient vectors.