Algorithms for multicriterion optimization

In this paper we report new methods for obtaining a noninferior solution to a multicriterion optimization problem. Unlike existing methods, which convert vector-valued optimization problems into scalar-valued ones, our methods treat the multiple objectives as they are, without employing an artificial single objective. Each of the new methods is readily implementable and convergent to a local noninferior solution under suitable assumptions. We also propose interactive methods for locating a noninferior solution that will be satisfactory to the decision maker.