Abstract
For the multiobjective problems, some global search methods may fail to find the Pareto optima with both accuracy and diversity. To pursue the two goals at the same time, a new memetic multiobjective differential evolution algorithm (MMODE) is proposed to hybridize the local search with differential evolution (DE) algorithm. The local search is conducted in an independent population to accelerate the search process, while DE can maintain the diversity. In MMODE, we use a new multiobjective Pareto differential evolution (MOPDE). Experimental results show that the MMODE performs better than other two MODEs in respects of the accuracy and diversity, especially for the multimodal functions.
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
Wu, Z., Chow, T.W.S.: A local multiobjective optimization algorithm using neighborhood field. Structural and Multidisciplinary Optimization 46(6), 853–871 (2012)
Coello, C.A.C., Lamont, G.B. (eds.): Application of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation), vol. 1. World Scientific Publishing Co. Pte. Inc. (2004)
Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. In: Proceedings of Evolutionary Methods for Design, Optimization and Control With Applications to Industrial Problems (EUROGEN), pp. 95–100 (September 2001)
Storn, R., Price, K.: Differential Evolution – a simple and efficient heuristic for global optimization over continuous space. J. of Global Optimization 11(4), 341–359 (1997)
Kukkonen, S., Lampinen, J.: Gde3: the third evolution step of generalized differential evolution. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 443–450 (September 2005)
Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)
Madavan, N.K.: Multiobjective optimization using a pareto differential evolution approach. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 1145–1150 (2002)
Abbass, H.: The self-adaptive pareto differential evolution algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 831–836 (2002)
Lara, A., Sanchez, G., Coello, C.A.C., Schutze, O.: HCS: A new local search strategy for memetic multiobjective evolutionary algorithms. IEEE Trans. on Evolutionary Computation 14(1), 112–132 (2010)
Soliman, O., Bui, L., Abbass, H.: A memetic coevolutionary multi-objective differential evolution algorithm. In: Multi-Objective Memetic Algorithms, pp. 369–388 (2009)
Veldhuizen, D.V., Lamont, G.: Multiobjective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03, Air Force Inst. Technol., Dayton, OH (1998)
Zhang, Q., Zhou, A., Zhao, S.Z., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the cec 2009 special session and competition. Technical Report CES-887, University of Essex and Nanyang Technological University (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Z., Xia, X., Zhang, J. (2013). MMODE: A Memetic Multiobjective Differential Evolution Algorithm. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-38703-6_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38702-9
Online ISBN: 978-3-642-38703-6
eBook Packages: Computer ScienceComputer Science (R0)