A linear bound on the number of scalarizations needed to solve discrete tricriteria optimization problems

TL;DR

This paper introduces a linear-bound method for solving discrete tricriteria optimization problems, reducing the number of scalarizations needed to find all nondominated points to at most three times their count.

Contribution

It presents a novel iterative procedure that guarantees a linear bound on the number of scalarizations for tricriteria problems, extending known bicriteria approaches.

Findings

Number of scalarizations is at most three times the nondominated points

Search space decomposition depends linearly on nondominated points

Method efficiently finds the entire nondominated set

Abstract

General multi-objective optimization problems are often solved by a sequence of parametric single objective problems, so-called scalarizations. If the set of nondominated points is finite, and if an appropriate scalarization is employed, the entire nondominated set can be generated in this way. In the bicriteria case it is well known that this can be realized by an adaptive approach which, given an appropriate initial search space, requires the solution of a number of subproblems which is at most two times the number of nondominated points. For higher dimensional problems, no linear methods were known up to now. We present a new procedure for finding the entire nondominated set of tricriteria optimization problems for which the number of scalarized subproblems to be solved is at most three times the number of nondominated points of the underlying problem. The approach includes an iterative update of the search space that, given a (sub-)set of nondominated points, describes the area in which additional nondominated points may be located. In particular, we show that the number of boxes, into which the search space is decomposed, depends linearly on the number of nondominated points.