Multiobjective differential evolution based on opposite operation

CIS 2009 - 2009 International Conference on Computational Intelligence and Security

Volumn 1, Issue , 2009, Pages 123-127

  Dong, Ning ^a   Wang, Yuping ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Differential evolution; Evolutionary algorithm; Multi objective optimization; Opposite operation

Indexed keywords

CANDIDATE SOLUTION; CONVERGENCE RATES; DIFFERENTIAL EVOLUTION; DIFFERENTIAL EVOLUTIONARY ALGORITHM; EVOLUTIONARY OPTIMIZATION ALGORITHM; INITIAL POPULATION; MULTI OBJECTIVE; MULTI-OBJECTIVE DIFFERENTIAL EVOLUTIONS;

ARTIFICIAL INTELLIGENCE; MULTIOBJECTIVE OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 77949296868     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIS.2009.166     Document Type: Conference Paper

References (15)

1. K. Miettinen, Nonlinear Multiobjectrive Optimization. Norwell, MA: Kluwer, 1999.
2. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
3. E. Zitzler, and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Computation, vol. 3, pp. 257-271, 1999.
4. Qingfu Zhang, and Hui Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Computation, vol. 11, no. 6, pp. 712-731, 2007.
5. E. Zitzler, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithm: Empirical Result. Evolutionary Computation, vol. 8, no.2, pp. 173-195, 2000.
6. J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms. In proceedings of the 1st Int. Conf. Genetic Algorithms, pp. 93-100, 1987.
7. R. Storn and K. Price, Differential evolution - a simple and efficient adaptative scheme for global optimization over continuous spaces. Technical Report TR-95-12, International Computer Science, Berkeley, California, 1995.
8. R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, vol.11, pp. 341-359, 1997.
9. 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 2001(CEC'2001), vol. 2, Piscataway, New Jersey, IEEE Service Center(2002), pp. 831-836.
10. S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, Opposition-based differential evolution. IEEE Trans. Evolutionary Computation, vol.12, No.1, pp. 64-79, Feb. 2008.
11. H. R. Tizhoosh, 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.
12. N. K. Madavan, Multiobjective optimization using a Pareto differential evolution approach. In CEC'2002, vol. 2, pp. 1145-1150, IEEE Service Center, 2002.
13. T. Tobic, and B. Filipic, DEMO: Differential evolution for multiobjective optimization. C.A.Coello Coello et al. (Eds.):EMO 2005, LNCS 3410, pp. 520-533.
14. E. Zitzler, K. Deb, and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical Results. Evolutioanary Computation, vol. 8, no. 2, pp. 173-195, 2000.
15. Qingfu Zhang, Aimin Zhou, and Yaochu Jin, RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans. Evolutionary Computation, vol. 12, no. 1, pp. 41-63, 2008.