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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References
Aleta A, Codina JM, Sanchez J, Gonzalez A (2001) Graph-Partitioning Based Instruction Scheduling for Clustered Processors. Proc. 34th ACM/EEE International Symposium on Microarchitecture, pp 150–159
Baños R, Gil C, Ortega J, Montoya FG (2004) Parallel Multilevel Metaheuristic for Graph Partitioning, J. of Heuristics, 10(3):315–336
Czyzak P, Jaszkiewicz A (1998) Pareto Simulated Annealing – a Metaheuristic Technique for Multiple-objective Combinatorial Optimization, J. of Multi-Criteria Decision Analysis 7:34–47
Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Company, San Francisco
Gil C, Ortega J, Montoya MG, Baños R, (2002) A Mixed Heuristic for Circuit Partitioning, Computational Optimization and Applications J. 23(3):321–340
Gil C, Ortega J, Montoya MG (2000) Parallel VLSI Test in a Shared Memory Multiprocessors, Concurrency: Practice and Experience, 12(5):311–326
Glover F, Laguna M (1993) Tabu Search. In: C.R. Reeves (eds) Modern Heuristic Techniques for Combinatorial Problems. Blackwell, London pp. 70–150
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley
Graph Partitioning Arch, http://staffweb.cms.gre.ac.uk/~c.walshaw/partition/
Hansen PH (1997) Tabu Search for Multiobjective Optimization: MOTS. Proc. of the 13th International Conference on Multiple Criteria Decision Making
Karypis G, Kumar V (1998) A Fast High Quality Multilevel Scheme for Partitioning Irregular Graphs. Tech. Report TR-95-035, University of Minnesota
Kirkpatrick S, Gelatt CD, Vecchi MP, (1983) Optimization by Simulated Annealing. Science 220(4598):671–680
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E, (1953) Equation of State Calculations by Fast Computing Machines. J. of Chemical Physics, 21(6):1087–1092
Rummler A, Apetrei A (2002) Graph Partitioning Revised – a Multiobjective Perspective. Proc. of 6th MultiConf. On Syst., Cybern. and Informatics
Selvakkumaran N, Karypis G (2003) Multi-Objective Hypergraph Partitioning Algorithms for Cut and Maximum Subdomain Degree Minimization. Proc. Int. Conference on Computer Aided Design, pp 726–733
Serafini P. (1993) Simulated Annealing for Multi-Objective Optimization problems. In G.H. Tzeng et al. (eds) Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, Springer Berlin, pp. 283–292
Ulungu E, Teghem J, Fortemps P, Tuyytens D (1999) MOSA Method: A Tool for Solving Multiobjective Combinatorial Optimization Problems. J. of Multi-Criteria Decision Analysis 8(4):221–236
Walshaw C, Cross M, Everett MG (1999) Mesh Partitioning and Load-balancing for Distributed Memory Parallel Systems. In: B. Topping (eds) Parallel & Distributed Proc. for Computational Mechanics: Systems and Tools, pp. 110–123
Zitzler E, Thiele L (1999) Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4):257–271
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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
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
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