- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2012 (2012), Article ID 172041, 12 pages
Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors
1Institute of Information and System Science, Beifang University of Nationalities, Yinchuan 750021, China
2Department of Mathematics, Yinchuan College, China University of Mining and Technology, Yinchuan 750011, China
Received 29 May 2012; Accepted 5 June 2012
Academic Editor: Yonghong Yao
Copyright © 2012 Yuelin Gao and Junmei Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- N. Srinivas and K. Deb, “Multi-objective optimization using non-dominated sorting in genetic algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, 1994.
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
- E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999.
- E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength Pareto evolutionary algorithm,” Tech. Rep. 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland, 2001.
- R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
- H. A. Abbass, R. Sarker, and C. Newton, “PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems,” in Proceedings of the Congress on Evolutionary Computation (CEC'01), pp. 971–978, May 2001.
- H. A. Abbass, “PDE: the self-adaptive Pareto differential evolution algorithm,” in Proceedings of the Congress on Evolutionary Computation (CEC'02), vol. 1, pp. 831–836, IEEE Service Center, Piscataway, NJ, USA, 2002.
- N. K. Madavan, “Multi-objective optimization using a Pareto differential evolution approach,” in Proceedings of the Congress on Evolutionary Computation (CEC'02), vol. 2, pp. 1145–1150, IEEE Service Center, Piscataway, NJ, USA, 2002.
- F. Xue, A.C. Sanderson, and R.J. Graves, “Pareto-based multi-objective differential evolution,” in Proceedings of the Congress on Evolutionary Computation (CEC'03), vol. 2, pp. 862–869, IEEE Press, Canberra, Australia, 2003.
- T. Robi and B. Filipi, “DEMO: differential evolution for multi- objective optimization,” in Lecture Notes in Computer Science, pp. 520–533, Springe, Berlin, Germany, 2005.
- W. Qian and A. li, “Adaptive differential evolution algorithm for multiobjective optimization problems,” Applied Mathematics and Computation, vol. 201, no. 1-2, pp. 431–440, 2008.
- W. Gong and Z. Cai, “An improved multiobjective differential evolution based on Pareto-adaptive ε-dominance and orthogonal design,” European Journal of Operational Research, vol. 198, no. 2, pp. 576–601, 2009.