Abstract
Multiobjective optimization is of increasing importance in various fields and has very broad applications. The purpose of this paper is to describe a novel multiobjective optimization algorithm–opposition-based multi-objective differential evolution algorithm(OMODE). In the paper, OMODE uses the opposition-based population to generate the initial population of points, The important scaling factor is controlled by self-adaptive method. Performance of OMODE is demonstrated with a set of benchmark test functions and Earth-Mars double transfer problem. The results show that OMODE achieves better performance than other methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Storn, R., Price, K.: Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: Congress on Evolutionary Computation (CEC 2002), Piscataway, New Jersey, vol. 1, pp. 831–836. IEEE Service Center, Los Alamitos (2002)
Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, vol. 2, pp. 862–869. IEEE Press, Los Alamitos (2003)
Robič, T., Filipič, B.: DEMO: Differential Evolution for Multi-objective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)
Babu, B.V., Mathew Leenus Jehan, M.: Differential Evolution for Multi-Objective Optimization. In: CEC 2003, Canberra, Australia, vol. 4, pp. 2696–2703 (December 2003)
Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 1145–1150 (2002)
Peng, L., Dai, G., Chen, F., Liu, F.: Study on Application of Multi-Objective Differential Evolution Algorithm in Space Rendezvous. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 46–52. Springer, Heidelberg (2007)
Tizhoosh, H.R.: Opposition-based learning: A new scheme for machine intelligence. In: Proc. Int. Conf. Comput. Intell. Modeling Control and Autom., Vienna, Austria. 2005, vol. I, pp. 695–701 (2005)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-Based Differential Evolution Algorithms. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 2010–2017 (2006)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGACII. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 173–195 (2000)
Zhang, J.: Research on Indicator-Based Evolutionary Algorithm and Its Application in Constellation Design.Master degree thesis. China University of Geosciences, Wuhan, China (2008)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) 8th Int’l. Conf. on Parallel Problem Solving from Nature (PPSN VIII), UK, pp. 832–842. Springer, Heidelberg (2004)
Myatt, D.R., Becerra, V.M., Nasuto, S.J., et al.: Advanced Global Optimization for Mission Analysis and Design, pp. 33–37, www.esa.in/act
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, L., Wang, Y., Dai, G. (2008). A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-92137-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
eBook Packages: Computer ScienceComputer Science (R0)