Adapting multi-objective meta-heuristics for graph partitioning

Abstract

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined, but rather a set of solutions, called Pareto-optimal front. Thus, the goal of multiobjective strategies is to generate a set of non-dominated solutions as an approximation to this front. This paper presents a novel adaptation of some of these metaheuristics to solve the multi-objective Graph Partitioning problem. © 2006 Springer.