Can single-objective optimization profit from multipleobjective optimization?

Abstract

Many real-world problems are multiobjective optimization problems, and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multiobjective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multiobjective model of the problem. That this is indeed the case is proved by investigating the single-source shortest paths problem and the computation of minimum spanning trees.