Multi-objective evolutionary algorithm for principal curve model based on multifractal

Jisuanji Yanjiu yu Fazhan/Computer Research and Development

Volumn 48, Issue 9, 2011, Pages 1729-1739

  Zhang, Dongmei ^a   Gong, Xiaosheng ^a   Dai, Guangming ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

MFPC MOEA; Model based multi objective evolutionary algorithms; Multifractal; Multifractal spectrum; Principal curve

Indexed keywords

MFPC-MOEA; MULTI FRACTALS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-FRACTAL SPECTRUM; PRINCIPAL CURVE;

BENCHMARKING; CONVERGENCE OF NUMERICAL METHODS; CURVE FITTING; FRACTALS; MULTIOBJECTIVE OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DISTRIBUTIONS;

EVOLUTIONARY ALGORITHMS;

EID: 80053961155     PISSN: 10001239     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. Deb K, Kalyanmoy D. Multi-objective optimization using evolutionary algorithms [M] New York: John Wiley & Sons, 2004
2. Luhua W. Relationship between dosimetric parameters and radiation pneumonitis in patients with non-small cell lung cancer who received postoperative radiotherapy [J]. International Journal of Radiation Oncology Biology Physics, 2008, 72 (2): 428-428
3. Coello C A C, Corte N C. Solving multiobjective optimization problems using an artificial immune system [J]. Genetic Programming and Evolvable Machines, 2005, 6 (2): 163-192
4. Coello C A C. Recent trends in evolutionary multiobjective optimization [J]. Evolutionary Multiobjective Optimization, 2005, 1 (1): 7-32
5. Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization [J]. IEEE Trans on Evolutionary Computation, 2004, 8 (3): 256-279
6. Laumanns M, Tantar E, Coello C A, et al. Computing gap free Pareto front approximations with stochastic search algorithms [J]. Evolutionary Computation, 2010, 18 (1): 65-96
7. Coello C A C, Jones A D, Gunaratne T C, et al. Atmospheric pressure femtosecond laser imaging mass spectrometry [J]. Analytical Chemistry, 2010, 82 (7): 2753-2758
8. Li Miqing, Zheng Jinhua, Luo Biao. A multi-objective evolutionary algorithm based on minimum spanning tree [J]. Journal of Computer Research and Development, 2009, 45 (5): 803-813 (in Chinese)
9. Zheng Jinhua, Luo Biao, Zhou Cong, et al. Searching for robust Pareto optimal solutions for MOPs with MOEA [J]. Acta Electronica Sinica, 2009, 37 (12): 2815-2822 (in Chinese)
10. Dai Guangming, Wang Maocai. Multiobjective Optimization Algorithm and Application of Satellite Constellation Design [M]. Wuhan: China University of Geosciences Press, 2009 (in Chinese)
11. Fonseca C M, Fleming P J. Genetic algorithm for multiobjective optimization: Formulation, discussion and generation [C] //Proc of the 5th Int Conf on Genetic Algorithms. San Mateo: Morgan Kauffmann, 1993: 416-423
12. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Trans on Evolutionary Computation, 2002, 6 (2): 182-197
13. Knowles J D, Corne D W. Approximating the nondominated front using the Pareto archived evolution strategy [J]. Evolutionary Computation, 1999, 8 (2): 149-172
14. Coello C A C, Pulido G T. A micro-genetic algorithm for multiobjective optimization [C] //Proc of the Genetic and Evolutionary Computation Conf. San Francisco: Morgan Kaufmann, 2001: 274-282
15. Liu Chunan, Wang Yuping. Dynamic multi-objective optimization evolutionary algorithm based on new model [J]. Journal of Computer Research and Development, 2008, 15 (4): 603-611 (in Chinese)
16. Gong Maoguo, Jiao Licheng, Yang Dongdong, et al. Research on evolutionary multi-objective optimization algorithms [J]. Journal of Software, 2009, 20 (2): 271-289 (in Chinese)
17. Pan Xiaoying, Jiao Licheng. A multi-agent social evolutionary algorithm for project optimization scheduling [J]. Journal of Computer Research and Development, 2008, 45 (6): 998-1003 (in Chinese)
18. Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm [G] //Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Berlin: Springer, 2002: 95-100
19. Dai Guangming, Wang Jianwen, Zhu Jiankai. A hybrid multi-objective algorithm using genetic and estimation of distribution based on design of experiments [C] //Proc of IEEE ICIS 2009. Piscataway, NJ: IEEE, 2009: 284-289
20. Kang Zhuo. Evolution programming and its application on modeling and forecasting [D]. Wuhan: Wuhan University, 2006 (in Chinese)
21. Zhou Aimin, Zhang Qingfu, Jin Yaochu, et al. Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover [C] // Proc of the 9th Annual Conf on Genetic and evolutionary computation. Piscataway, NJ: IEEE, 2007: 617-623
22. Zhang Qingfu, Li Hui. MOEA/D: A multiobjective evolutionary algorithm based on decomposition [J]. IEEE Trans on Evolutionary Computation, 2007, 11 (6): 712-731
23. Zhang Qingfu, Zhou Aimin, Jin Yaochu. RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm [J]. IEEE Trans on Evolutionary Computation, 2008, 12 (1): 41-63
24. Hastie T. Principal curves and surfaces [R]. Stanford: Department of Statistics, Stanford University, 1984
25. Suman B. Study of self-stopping PDMOSA and performance measure in multiobjective optimization [J]. Computers & Chemical Engineering, 2005, 29 (5): 1131-1147
26. Marti L, Garca J, Berlanga A, et al. A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms [C] //Proc of Gecco 2007: Genetic and Evolutionary Computation Conf. New York: ACM, 2007: 2835-2842
27. Zhou Aimin, Jin Yaochu, Zhang Qingfu, et al. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion [C] //Proc of 2006 IEEE Congress on Evolutionary Computation. Piscataway, NJ: IEEE, 2006: 892-899
28. Mandelbrot B B. Possible refinement of the lognormal hypothesis concerning the distribution of energy dissipation in intermittent turbulence [G] //Lecture Notes in Physics 12. Berlin: Springer, 1972: 333-351
29. Cheng Qiuming, Zhang Shengyuan, Zuo Renguang, et al. Progress of multifractal filtering techniques and their applications in geochemical imformation extraction [J]. Earth Science Frontiers, 2009, 16 (2): 185-198 (in Chinese)
30. Cheng Qiuming, Zhao Pengda, Zhang Shengyuan, et al. Application of singularity theory in prediction of tin and copper mineral deposits in Gejiu District, Yunnan, China: Information integration and delineation of mineral exploration targets [J]. Journal of China University of Geosciences: Earth Science, 2009, 34 (2): 243-252 (in Chinese)
31. Arneodo A, Audit B, Brodie B E B, et al. Fractals and wavelets: What can we learn on transcription and replication from wavelet-based multifractal analysis of DNA sequences [G] //Encyclopedia of Complexity and Systems Science 2009. Berlin: Springer, 2009: 3893-3924
32. Jiménez-Hornero F J, Autiérrez E, Ariza-Billarverde A B. Description of the seasonal pattern in ozone concentration time series by using the strange attractor multifractal formalism [J]. Environmental Monitoring and Assessment, 2010, 160 (1): 229-236
33. Deb K. Multi-objective genetic algorithms: Problem difficulties and construction of test problems [J]. Evolutionary Computation, 1999, 7 (3): 205-230
34. Deb K, Thiele L, Laumanns M, et al. Scalable multi-objective optimization test problems [C] //Proc of Congress on Evolutionary Computation (CEC2002). Piscataway, NJ: IEEE, 2002: 825-830