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IMPROVING THE PERFORMANCE OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS USING THE ISLAND PARALLEL MODEL

Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain

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C. GIL

Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain

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R. BAÑOS

Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain

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, and

J. GÓMEZ

Dept. Lenguajes y Computación, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain

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https://doi.org/10.1142/S0129626407002922Cited by:3 (Source: Crossref)

Abstract

Recently, the research interest in multi-objective optimization has increased remarkably. Most of the proposed methods use a population of solutions that are simultaneously improved trying to approximate them to the Pareto-optimal front. When the population size increases, the quality of the solutions tends to be better, but the runtime is higher. This paper presents how to apply parallel processing to enhance the convergence to the Pareto-optimal front, without increasing the runtime. In particular, we present an island-based parallelization of five multi-objective evolutionary algorithms: NSGAII, SPEA2, PESA, msPESA, and a new hybrid version we propose. Experimental results in some test problems denote that the quality of the solutions tends to improve when the number of islands increases.

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References

D. E. Goldberg , Genetic Algorithms in Search, Optimization and Machine Learning ( Addison Wesley , New York , 1989 ) . Google Scholar
C. A. Coello , D. A. Van Veldhuizen and G. B. Lamont , Evolutionary Algorithms for Solving Multi-Objective Problems ( Kluwer Academic Publishers , 2002 ) . Crossref, Google Scholar
M. R. Garey and D. S. Johnson , Computers and Intractability: A Guide to the Theory of NP-Completeness ( W.H. Freeman & Company , San Francisco , 1979 ) . Google Scholar
E. Zitzler and L. Thiele, IEEE Transactions on Evolutionary Computation 3(4), 257 (1999), DOI: 10.1109/4235.797969. Crossref, Web of Science, Google Scholar
K. Deb , Multi-Objective Optimization using Evolutionary Algorithms ( John Wiley & Sons , New York , 2001 ) . Google Scholar
M. Laumanns, E. Zitzler and L. Thiele, Springer-Lecture Notes in Computer Science 1993 (2001) pp. 181–196. Google Scholar
K. Debet al., Springer-Lecture Notes in Computer Science 1917 (2000) pp. 849–858. Google Scholar
E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss federal Institute of Technology (ETH) Zurich, 2001 . Google Scholar
D. W. Corne, J. D. Knowles and M. J. Oates, Springer-Lecture Notes in Computer Science 1917 (2000) pp. 839–848. Google Scholar
C. Gilet al., Global Multiobjective Optimization using Evolutionary Methods: an Experimental Analysis, International Workshop on Global Optimization (2005) pp. 115–120. Google Scholar
J. D. Knowles and D. W. Corne, The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation, Congress on Evolutionary Computation1 (IEEE Press, 1999) pp. 98–105, DOI: 10.1109/CEC.1999.781913. Google Scholar
D. A. Veldhuizen, J. B. Zydallys and G. B. Lamont, IEEE Transactions on Evolutionary Computation 7(2), 144 (2003), DOI: 10.1109/TEVC.2003.810751. Crossref, Web of Science, Google Scholar
E. Alba and M. Tomassini, IEEE Transactions on Evolutionary Computation 6(5), 443 (2002), DOI: 10.1109/TEVC.2002.800880. Crossref, Web of Science, Google Scholar
R. Bañoset al., Journal of Heuristics 10(3), 315 (2004), DOI: 10.1023/B:HEUR.0000026898.11874.e7. Crossref, Web of Science, Google Scholar
E. Zitzler, K. Deb and L. Thiele, Evolutionary Computation 8(3), 173 (2000), DOI: 10.1162/106365600568202. Crossref, Web of Science, Google Scholar
D. A. Veldhuizen, Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations, PhD thesis, Dpt. Electrical and Comp. Eng., Ohio, 1999 . Google Scholar
K. Debet al., Constrained Test Problems for Multi-Objective Evolutionary Optimization, First International Conference on Evolutionary Multi-Criterion Optimization (2001) pp. 248–298. Google Scholar
C. A. Coello Coello , D. A. Van Veldhuizen and G. B. Lamont , Evolutionary Algorithms for Solving Multi-objective Problems ( Kluwer Academic Publishers , New York , 2002 ) . Crossref, Google Scholar
F. Burk , Lebesgue Measure And Integration: An Introduction ( John Wiley & Sons Inc , 2005 ) . Google Scholar