An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover

2009 IEEE Congress on Evolutionary Computation, CEC 2009

Volumn , Issue , 2009, Pages 1959-1964

  Gao, Song ^a   Zeng, Sanyou ^a   Xiao, Bo ^a   Zhang, Lei ^a   Shi, Yulong ^a   Tian, Xin ^a   Yang, Yang ^a   Long, Haoqiu ^a   Yang, Xianqiang ^a   Yu, Danping ^a   Yan, Zu ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONAL CROSSOVER; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; ORTHOGONAL CROSSOVER; THREE PROBLEMS;

MATHEMATICAL OPERATORS; MULTIOBJECTIVE OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 70449857965     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2009.4983180     Document Type: Conference Paper

References (17)

1. Qingfu Zhang, Aimin Zhou, S. Zhao, P. N. Suganthan, Wudong Liu, Santosh Tiwari, Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition
2. Sanyou Zeng, Shuzhen Yao, Lishan Kang, and Yong Liu, An Efficient Multi-objective Evolutionary Algorithm: OMOEA-II, Springer-Verlag Berlin Heidelberg 2005, pp.108-119, 2005
3. Schaffer, J. D. (1984).Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Ph. D. thesis, Vanderbilt University. Unpublished.
4. Hajela, P. and Lin, C. Y. (1992). Genetic search strategies in multicriterion optimal design. Structural Optimization 4, 99C107.
5. Fonseca, C. M. and Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, pp. 416C423. Morgan Kaufmann.
6. Horn, J. and Nafpliotis, N. (1993). Multiobjective optimization using the niched pareto genetic algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign.
7. Knowles, J.D., Corne, D.W.(2000): Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149C172.
8. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182-197.
9. Eckart Zitzler, Marco Laumanns, and Lothar Thiele(2002). SPEA2: Improving the Strength. Pareto Evolutionary Algorithm for Multiobjective Optimization. In K.C. Giannakoglou et al., editor, Proceedings of the EUROGEN2001 Conference, pages 95-100, Barcelona, Spain, CIMNE
10. Purshouse, R. C., Fleming, P. J. (2001). The Multi-objective Genetic Algorithm Applied to Benchmark ProblemsCan Analysis. Research Report No. 796. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK.
11. Tiwari, A., Roy, R.(2002). Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications. Proceedings of the seventh international conference on parallel problem solving from nature. ( PPSN VII). Granada, Spain
12. Q. Zhang, H Li. MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition, IEEE Trans. on Evolutionary Computation, vol.11, no.6, pp712-731, 2007. (Pubitemid 350187486)
13. Wu, Q.(1978). On the optimality of orthogonal experimental design. Acta Math. Appl Sinica, 1(4), pp. 283-299.
14. Q. Zhang and Y. W. Leung, Orthogonal Genetic Algorithm for Multimedia Multicast Routing. , IEEE Trans on Evolutionary Computation, Vol.3, No.1, 1999.
15. Jinn-Tsong Tsai, Tung-Kuan Liu, and Jyh-Horng Chou (2004). Hybrid Taguchi- Genetic Algorithm for Global Numerical Optimization, IEEE Transactions on Evolutionary Computation, 8(4), pp365-377.
16. Sanyou Y.Zeng, Lishan S.Kang, Lixing X.Ding (2004). An Orthogonal Multiobjective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints. Evolutionary Computation, 12(1), pp77-98.
17. Montgomery, D. C.(1991), Design and Analysis of Experiments. 3rd ed., New York: Wiley.