Abstract
We study a coarse grained parallelization scheme (thread based) aimed at solving complex multi-objective problems by means of decomposition. Our scheme is loosely based on the MOEA/D framework. The resulting algorithm, called Parallel Decomposition (PaDe), makes use of the asynchronous generalized island model to solve the various decomposed problems. Efficient exchange of chromosomic material among islands happens via a fixed migration topology defined by the proximity of the decomposed problem weights. Each decomposed problem is solved using a generic single objective evolutionary algorithm (in this paper we experiment with self-adaptive differential evolution (jDE)). Comparing our algorithm to MOEA/D-DE we find that it is attractive in terms of performances and, most of all, in terms of computing time. Experiments with increasing numbers of threads show that PaDe scales well, being able to fully exploit the number of underlying available cores.
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References
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the EUROGEN 2001 Conference (2001)
Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Evolutionary Computation 11(6), 712–731 (2007)
Miettinen, K.: Nonlinear Multiobjective Optimization. International series in operations research & management science. Kluwer Academic Publishers (1999)
Das, I., Dennis, J.: Normal-boundary intersection: An alternate method for generating pareto optimal points in multicriteria optimization problems (1996)
Jiang, S., Cai, Z., Zhang, J., Ong, Y.S.: Multiobjective optimization by decomposition with pareto-adaptive weight vectors. In: Seventh International Conference on Natural Computation, ICNC 2011 (2011)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13(2), 284–302 (2009)
Al Moubayed, N., Petrovski, A., McCall, J.: A novel smart multi-objective particle swarm optimisation using decomposition. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 1–10. Springer, Heidelberg (2010)
Nebro, A.J., Durillo, J.J.: A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 303–317. Springer, Heidelberg (2010)
Durillo, J.J., Zhang, Q., Nebro, A.J., Alba, E.: Distribution of Computational Effort in Parallel MOEA/D. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 488–502. Springer, Heidelberg (2011)
Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer (2005)
Izzo, D., Ruciński, M., Biscani, F.: The generalized island model. In: Fernandez de Vega, F., Hidalgo Pérez, J.I., Lanchares, J. (eds.) Parallel Architectures & Bioinspired Algorithms. SCI, vol. 415, pp. 151–170. Springer, Heidelberg (2012)
Mambrini, A., Sudholt, D., Yao, X.: Homogeneous and heterogeneous island models for the set cover problem. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 11–20. Springer, Heidelberg (2012)
PaGMO: Parallel Global Multiobjective Optimizer, http://pagmo.sourceforge.net/pagmo/
PyGMO: Python Parallel Global Multiobjective Optimizer, http://pagmo.sourceforge.net/pygmo/
Halton, J.H.: Algorithm 247: Radical-inverse quasi-random point sequence. Commun. ACM 7(12), 701–702 (1964)
Märtens, M., Izzo, D.: The asynchronous island model and NSGA-II: study of a new migration operator and its performance. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 1173–1180. ACM (2013)
Brest, J., Zumer, V., Maucec, M.S.: Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 215–222. IEEE (2006)
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Mambrini, A., Izzo, D. (2014). PaDe: A Parallel Algorithm Based on the MOEA/D Framework and the Island Model. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_70
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DOI: https://doi.org/10.1007/978-3-319-10762-2_70
Publisher Name: Springer, Cham
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