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.
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Baños, R., Gil, C., Montoya, M., Ortega, J. (2006). Adapting Multi-Objective Meta-Heuristics for Graph Partitioning. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_10
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DOI: https://doi.org/10.1007/3-540-31662-0_10
Publisher Name: Springer, Berlin, Heidelberg
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