Abstract
This paper introduces a novel Parallel Multi-Objective Evolutionary Algorithm (pMOEA) which is based on the island model. The serial algorithm on which this approach is based uses the differential evolution operators as its search engine, and includes two mechanisms for improving its convergence properties (through local dominance and environmental selection based on scalar functions). Two different parallel approaches are presented. The first aims at improving effectiveness (i.e., for better approximating the Pareto front) while the second aims to provide a better efficiency (i.e., by reducing the execution time through the use of small population sizes in each sub-population). To assess the performance of the proposed algorithms, we adopt a set of standard test functions and performance measures taken from the specialized literature. Results are compared with respect to its serial counterpart and with respect to three algorithms representative of the state-of-the-art in the area: NSGA-II, MOEA/D and MOEA/D-DE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007) ISBN 978-0-387-33254-3
Arias Montaño, A., Coello Coello, C.A., Mezura-Montes, E.: MODE-LD+SS: A Novel Differential Evolution Algorithm Incorporating Local Dominance and Scalar Selection Mechanisms for Multi-Objective Optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain. IEEE Press, Los Alamitos (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11, 712–731 (2007)
Li, H., Zhang, Q.: Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13, 284–302 (2009)
Talbi, E.G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., Coello Coello, C.: Parallel approaches for multi-objective optimization. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 349–372. Springer, Heidelberg (2008)
Nebro, A., Luna, F., Talbi, E.G., Alba, E.: Parallel Multiobjective Optimization. In: Alba, E. (ed.) Parallel Metaheuristics, pp. 371–394. Wiley-Interscience, New Jersey (2005) ISBN 13-978-0-471-67806-9
Kamiura, J., Hiroyasu, T., Miki, M., Watanabe, S.: MOGADES: Multi-objective genetic algorithm with distributed environment scheme. In: Computational Intelligence and Applications (Proceedings of the Second International Workshop on Intelligent Systems Design and Applications: ISDA 2002), pp. 143–148 (2002)
Streichert, F., Ulmer, H., Zell, A.: Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 92–107. Springer, Heidelberg (2005)
Zaharie, D., Petcu, D.: Adaptive pareto differential evolution and its parallelization. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2004. LNCS, vol. 3019, pp. 261–268. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arias Montaño, A., Coello Coello, C.A., Mezura-Montes, E. (2010). pMODE-LD+SS: An Effective and Efficient Parallel Differential Evolution Algorithm for Multi-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)