Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

Science in China, Series F: Information Sciences

Volumn 51, Issue 8, 2008, Pages 1064-1082

  Gong, MaoGuo ^a   Jiao, LiCheng ^a   Ma, WenPing ^a   Du, HaiFeng ^a,b  

^a XIDIAN UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Artificial immune systems; Clonal selection; Evolutionary algorithms; Immunodominance; Multiobjective optimization

Indexed keywords

EID: 46749113373     PISSN: 10092757     EISSN: 18622836     Source Type: Journal    
DOI: 10.1007/s11432-008-0040-2     Document Type: Article

References (53)

1. Fonseca C M, Fleming P J. An overview of evolutionary algorithms in multiobjective optimization. Evol Comp, 1995, 3(1): 1-16
2. Schaffer J D. Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Nashville, TN: Vanderbilt University, 1984.
3. Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: equation, discussion and generalization. In: Proc 5th Intern Conf Genetic Algor. San Mateo, California, 1993. 416-423
4. Horn J, Nafpliotis N. Multiobjective optimization using the niched pareto genetic algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign, 1993.
5. Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comp, 1994, 2(3): 221-248
6. Coello C A. Evolutionary multiobjective optimization: current and future challenges. In: Adv Soft Computing-Eng, Design and Manuf. Berlin: Springer-Verlag, 2003. 243-256
7. Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comp, 1999, 3(4): 257-271
8. Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. In: Evolut Methods Design, Optim and Control with Appl Industrial Problems, Athens, Greece, 2002. 95-100
9. Knowles J D, Corne D W. Approximating the nondominated front using the Pareto archived evolution strategy. Evol Comp, 2000, 8(2): 149-172
10. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput, 2002, 6(2): 182-197
11. Van Veldhuizen D A, Lamont G B. Multiobjective optimization with Messy genetic algorithms. In: Proc 2000 ACM Symposium on Applied Computing, Villa Olmo, Como, Italy, 2000. 470-476
12. Zydallis J. Explicit building-block multiobjective genetic algorithms: theory, analysis, and development. PhD thesis, Air Force Institute of Technology, Wright Patterson AFB, OH, March 2003
13. Coello Coello C A, Pulido G T. Multiobjective optimization using a micro-genetic algorithm. In: Proc Genetic Evolut Computation Conf (GECCO-2001), Morgan Kaufmann Publishers, San Francisco, California, 2001. 274-282
14. Tarakanov A, Dasgupta D. A formal model of an artificial immune system. BioSystems, 2000, 55(1/3): 151-158
15. de Castro L N, Timmis J. Artificial immune systems: a new computational intelligence approach. Heidelberg: Springer-Verlag, 2002. ISBN: 1-85233-594-7.
16. Ishida Y. Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model. In: Proc Intern Joint Conf Neural Networks. San Diego, 1990. 777-782
17. Forrest S, Perelson A S, Allen L,et al. Self-nonself discrimination in a computer. In: Proc IEEE Symp Research Security and Privacy, IEEE Computer Society Press, Los Alamitos, CA, 1994. 202-212
18. Gonzalez F, Dasgupta D, Kozma R. Combining negative selection and classification techniques for anomaly detection. In: Proc Special Sessions Artif Immune Syst Congress on Evolut Computation, IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, May 2002
19. Carter J H. The immune system as a model for pattern recognition and classification. J American Medical Inform Assoc, 2000, 7(3): 28-41
20. Timmis J, Neal M, Hunt J. An artificial immune system for data analysis. Biosystems, 2000, 55(1/3): 143-150
21. White J A, Garrett S M. Improved pattern recognition with artificial clonal selection. In: Proc Second Intern Conf Artif Immune Syst (ICARIS). Napier University, Edinburgh, UK, September 1-3, 2003
22. Hart E, Ross P. The evolution and analysis of a potential antibody library for use in Job-shop scheduling. A chapter in the book "New Ideas in Optimization". New York: McGraw-Hill, 1999. 185-202
23. Coello Coello C A, Rivera D C, Cruz Cortes N. Use of an artificial immune system for Job shop scheduling. In: Proc Second Intern Conf Artif Immune Syst (ICARIS). Napier University, Edinburgh, UK, September 1-3, 2003
24. Jiao L C, Wang L. A novel genetic algorithm based on immunity. IEEE Trans Syst, Man and Cybern, Part A, 2000, 30(5): 552-561
25. de Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle. IEEE Trans Evol Comp, 2002, 6(3): 239-251
26. Du H F, Gong M G, Liu R C, et al. Adaptive chaos clonal evolutionary programming algorithm. Sci China Ser F-Inf Sci, 2005, 48(5): 579-595
27. Hajela P, Yoo J, Lee J. GA based simulation of immune networks-applications in structural optimization. J Eng Optim, 1997
28. Gong M G, Du H F, Jiao L C. Optimal approximation of linear systems by artificial immune response. Sci China Ser F-Inf Sci, 2006, 49(1): 63-79
29. Zitzler E. Evolutionary algorithms for multiobjective optimization: methods and applications. A dissertation submitted to the Swiss Federal Institute of Technology Zurich for the degree of Doctor of Technical Sciences. Diss. Eth No. 13398. 1999
30. Coello Coello C A, Cruz Cortss N. Solving multiobjective optimization problems using an artificial immune system. Genetic Progr and Evolv Mach, 2005, 6: 163-190
31. Abbas A K, Lichtman A H, Pober J S. Cellular and molecular immunology. 4th ed. New York: W B Saunders Co., 2000
32. Yewdell J W, Bennink J R. Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Ann Rev Immun, 1999, 17: 51-88
33. Hart E, Timmis J. Application areas of AIS: the past, the present and the future. In: Proc 4th Intern Conf Artif immune Syst, ICARIS 2005. Springer, Lecture Notes in Computer Science, 2005, 3627: 483-497
34. de Castro L N, Timmis J. An artificial immune network for multimodal fnction optimization. In: Proc 2002 Congress on Evolut Computation, CEC' 02, 2002. 699-704
35. Garrett S M. Parameter-free, adaptive clonal selection. In: Proc IEEE Congress Evolut Computing, CEC 2004, Portland, Oregon, 2004. 1052-1058.
36. Cutello V, Narzisi G, Nicosia G, et al. Clonal selection algorithms: a comparative case study using effective mutation potentials. In: Proc 4th Intern Conf Artif Immune Syst, ICARIS 2005, Banff, Canada. August 14-17, 2005, Lecture Notes in Computer Science, 2005, 3627: 13-28
37. Cutello V, Nicosia G, Pavone M. Exploring the capability of immune algorithms: a characterization of hypemutation operators. In: Proc Third Intern Conf Artif Immune Syst, ICARIS2004, Catania, Italy, September 13-16, 2004, Lecture Notes in Computer Science, 2004, 3239: 263-276
38. Coello Coello C A, Cortes N C. An approach to solve multiobjective optimization problems based on an artificial immune system. In: Proc First Intern Conf Artif Immune Syst, ICARIS2002, University of Kent at Canterbury, UK, September, 9-11, 2002. 212-221
39. Freschi F, Repetto M. Multiobjective optimization by a modified artificial immune system algorithm. In: Proc 4th Intern Conf on artif immune syst, ICARIS 2005. Springer, Lecture Notes in Comput Sci, 2005, 3627: 248-261
40. Cutello V, Narzisi G, Nicosia G. A class of Pareto archived evolution strategy algorithms using immune inspired operators for Ab-Initio protein structure prediction. In Third European Workshop on Evolut Computation and Bioinform, EvoWorkshops 2005-EvoBio 2005, Lausanne, Switzerland, 30 March-April 2005, Lecture Notes in Comput Sci, 2005, 3449: 54-63
41. Farmer J D, Packard N H, Perelson A S. The immune system, adaptation, and machine learning. Physica D, 1986, 2(1-3): 187-204
42. Goldberg D E. Genetic algorithms in search, optimization, and machine learning, reading. Massachusetts: Addison-Wesley, 1989.
43. Morse J N. Reducing the size of the nondominated set: pruning by clustering. Comput Oper Res, 1980, 7(1-2): 55-66
44. Rosenman M A, Gero J S. Reducing the Pareto optimal set in multicriteria optimization. Eng Optim, 8, 1985: 189-206
45. Van Veldhuizen D A. Multiobjective evolutionary algorithms: classification, analyses, and new innovations. Air Force Institute of Technology. AFIT/DS/ENG/99-01, 1999
46. Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comp, 2000, 8(2): 173-195
47. Zitzler E, Thiele L, Laumanns M, et al. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comp, 2003, 7(2): 117-132
48. Deb K. Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comp, 1999, 7(3): 205-230
49. Jiao L C, Gong M G, Shang R H, et al. Clonal selection with immune dominance and anergy based multiobjective optimization. In: Proc Third Intern Conf Evolut Multi-Criterion Optim, EMO 2005, Guanajuato, Mexico, March 9-11, 2005. Springer, Lecture Notes in Comput Sci, 2005, 3410: 474-489
50. Hajela P, Lin C Y. Genetic search strategies in multicriterion optimal design. Struct Optim, 1992, 4: 99-107
51. Fieldsend J, Everson R M, Singh S. Using unconstrained elite archives for multiobjective optimization. IEEE Trans Evol Comp, 2003, 7(3): 305-323
52. Chambers J M, Cleveland W S, Kleiner B, et al. Graphical methods for data analysis. Wadsworth & Brooks/Cole Publishing Company, Pacific Grove, California, 1983
53. Khare V, Yao X, Deb K. Performance scaling of multi-objective evolutionary algorithms. In: Proc Second Intern Conf Evolut Multi-Criterion Optim, EMO 2003, Springer-Verlag, Lecture Notes in Comput Sci, 2003, 2632: 376-390