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Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Abstract

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performan ce comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

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Reference

  1. Fonseca C M, Fleming P J. An overview of evolutionary algorithms in multiobjective optimization. Evol Comp, 1995, 3(1): 1–16

    Article  Google Scholar 

  2. Schaffer J D. Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Nashville, TN: Vanderbilt University, 1984.

    Google Scholar 

  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.

    Google Scholar 

  5. Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comp, 1994, 2(3): 221–248

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  15. de Castro L N, Timmis J. Artificial immune systems: a new computational intelligence approach. Heidelberg: Springer-Verlag, 2002. ISBN: 1-85233-594-7.

    MATH  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  20. Timmis J, Neal M, Hunt J. An artificial immune system for data analysis. Biosystems, 2000, 55(1/3): 143–150

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  31. Abbas A K, Lichtman A H, Pober J S. Cellular and molecular immunology. 4th ed. New York: W B Saunders Co., 2000

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  42. Goldberg D E. Genetic algorithms in search, optimization, and machine learning, reading. Massachusetts: Addison-Wesley, 1989.

    Google Scholar 

  43. Morse J N. Reducing the size of the nondominated set: pruning by clustering. Comput Oper Res, 1980, 7(1–2): 55–66

    Article  Google Scholar 

  44. Rosenman M A, Gero J S. Reducing the Pareto optimal set in multicriteria optimization. Eng Optim, 8, 1985: 189–206

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  48. Deb K. Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comp, 1999, 7(3): 205–230

    Article  Google Scholar 

  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

    Google Scholar 

  50. Hajela P, Lin C Y. Genetic search strategies in multicriterion optimal design. Struct Optim, 1992, 4: 99–107

    Article  Google Scholar 

  51. Fieldsend J, Everson R M, Singh S. Using unconstrained elite archives for multiobjective optimization. IEEE Trans Evol Comp, 2003, 7(3): 305–323

    Article  Google Scholar 

  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

    MATH  Google Scholar 

  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

    Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China

    MaoGuo Gong, LiCheng Jiao, WenPing Ma & HaiFeng Du

  2. School of Public and Administration, Xi’an Jiaotong University, Xi’an, 710049, China

    HaiFeng Du

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  1. MaoGuo Gong
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  2. LiCheng Jiao
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  3. WenPing Ma
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  4. HaiFeng Du
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Correspondence to MaoGuo Gong.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 60703107 and 60703108), the National High Technology Research and Development Program (863 Program) of China (Grant No. 2006AA01Z107), the National Basic Research Program (973 Program) of China (Grant No. 2006CB705700) and the Program for Cheung Kong Scholars and Innovative Research Team in University (Grant No. IRT0645)

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Gong, M., Jiao, L., Ma, W. et al. Multiobjective optimization using an immunodominance and clonal selection inspired algorithm. Sci. China Ser. F-Inf. Sci. 51, 1064–1082 (2008). https://doi.org/10.1007/s11432-008-0040-2

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  • DOI: https://doi.org/10.1007/s11432-008-0040-2

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