Regularities of Pareto sets in low-dimensional practical multi-criteria optimisation problems: analysis, explanation, and exploitation

Abstract

AbstractMany practical optimisation problems have conflicting objectives, which should be addressed by multi-criteria optimisation (MCO), i.e. by determining the set of best compromises, the Pareto set (PS), along with its picture in parameter space (PSPS). In previous work on low-dimensional MCO problems, we have found characteristic topological features of the PS and PSPS, which depend on the dimensionality of the parameter space M and the objective space N. E.g., $$M = 2$$ M = 2 and $$N = 3$$ N = 3 yields triangles with needle-like extensions. The reasons for these topological features were unknown so far. Here, we show that they are to be expected if all objective functions of the MCO satisfy two conditions: (a) they can be approximated by quadratic functions and (b) one of the eigenvalues of the Hessian matrix evaluated at the function’s minimum is small compared to the other eigenvalues. Objective functions which meet conditions (a) and (b) have a valley-like topology, for which the valley lies in the direction of the eigenvector corresponding to the lowest eigenvalue. The PSPS can be estimated by starting at the minimum of an objective function, following the valley, and combining these lines for all objective functions. The PS is obtained by evaluating the objective functions. We believe that the conditions (a) and (b) are met in many practical problems and discuss an example from molecular modelling. The improved understanding of the features of these MCO problems opens the route for designing methods for swiftly finding estimates of their PS and PSPS.