The Design of Self-evolving Artificial Immune System II for Permutation Flow-shop Problem
Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.
[1] J. T. Tsai, W. H. Ho, T. K. Liu, and J. H. Chou, "Improved immune
algorithm for global numerical optimization and job-shop scheduling
problems," Applied Mathematics and Computation, Vol. 194, pp.
406-424, Dec. 2007.
[2] J. S. Chun, H. K. Jung and S. Y. Hahn, "A Study on Comparison of
Optimization Performances between Immune Algorithm and other
Heuristic Algorithms," IEEE Transactions on Magnetics, vol. 34, No. 5,
Sept. 1998.
[3] J. H. Holland, "Genetic Algorithms and the Optimal Allocation of
Trials," SIAM J. Comput, vol. 2, pp. 88-105, 1973.
[4] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning (Book style). Boston, MA: Addison-Wesley, 1989.
[5] F. Campelo, F. G. Guimar˜aes, and H. Igarashi, "Overview of Artificial
Immune Systems for Multi-objective Optimization," Lecture Notes in
Computer Science, vol. 4403 , pp. 937-951, 2007.
[6] P. C. Chang, W. H. Huang, and C. J. Ting, "A hybrid genetic-immune
algorithm with improved lifespan and elite antigen for flow-shop
scheduling problems," International Journal of Production Research, vol.
49, pp. 937-951, Sept. 2007.
[7] K. C. Tan, C. K. Goh, A. A. Mamun, and E. Z. Ei, "An evolutionary
artificial immune system for multi-objective optimization," European
Journal of Operational Research, vol. 187, pp. 371-392, Jun. 2008.
[8] T. Bagchi, Multiobjective Scheduling by Genetic Algorithms (Book
style). New York, 1999.
[9] C. R. Reeves, "A Genetic Algorithm for Flowshop Sequencing,"
Computers and Operations Research, vol. 5, pp. 5-13, Jan. 1995.
[10] J. D. Farmer, N. Packard, and A. Perelson, "The immune system,
adaptation and machine learning," Physica D, vol. 2, pp. 187-204,
Oct.-Nov. 1986.
[11] H. Bersini, and F. J. Varela, "Hints for adaptive problem solving gleaned
from immune networks," Parallel Problem Solving from Nature, vol. 496,
pp. 343-354, 1991.
[12] J. O. Kephart, "A biologically inspired immune system for computers,"
Proceedings of Artificial Life IV: The Fourth International Workshop on
the Synthesis and Simulation of Living Systems, MIT Press. pp. 130-139,
1994.
[13] D. Dasgupta, Artificial Immune Systems and Their Applications (Book
style). Berlin : Springer-Verlag, Jan. 1999.
[14] L. N. de Castro and F. J. Von Zuben, Artificial Immune Systems: Part I
-Basic Theory and Applications (Book style). Brazil, 1999.
[15] V. Cutello and G. Nicosia, "An Immunological Approach to
Combinatorial Optimization Problems," Lecture Notes in Computer
Science, vol. 2527, pp. 361-370, 2002.
[16] V. Cutello, G. Nicosia, M. Pavone, and J. Timmis, "An Immune
Algorithm for Protein Structure Prediction on Lattice Models," IEEE
Transactions on Evolutionary Computation, vol. 11 , pp. 101-117, 2007.
[17] J. Zhang, C. Zhang, and S. Liang, "The circular discrete particle swarm
optimization algorithm for flow shop scheduling problem," Expert
Systems with Applications, vol. 37, pp. 5827-5834, 2010.
[18] P. C. Chang, W. H. Huang, and C. J. Ting, "Self-evolving Artificial
Immune System via Developing T and B Cell for Permutation Flow-shop
Scheduling Problems," Proceedings of World Academy of Science,
Engineering and Technology, vol. 65, pp. 822-827, May 2010.
[1] J. T. Tsai, W. H. Ho, T. K. Liu, and J. H. Chou, "Improved immune
algorithm for global numerical optimization and job-shop scheduling
problems," Applied Mathematics and Computation, Vol. 194, pp.
406-424, Dec. 2007.
[2] J. S. Chun, H. K. Jung and S. Y. Hahn, "A Study on Comparison of
Optimization Performances between Immune Algorithm and other
Heuristic Algorithms," IEEE Transactions on Magnetics, vol. 34, No. 5,
Sept. 1998.
[3] J. H. Holland, "Genetic Algorithms and the Optimal Allocation of
Trials," SIAM J. Comput, vol. 2, pp. 88-105, 1973.
[4] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning (Book style). Boston, MA: Addison-Wesley, 1989.
[5] F. Campelo, F. G. Guimar˜aes, and H. Igarashi, "Overview of Artificial
Immune Systems for Multi-objective Optimization," Lecture Notes in
Computer Science, vol. 4403 , pp. 937-951, 2007.
[6] P. C. Chang, W. H. Huang, and C. J. Ting, "A hybrid genetic-immune
algorithm with improved lifespan and elite antigen for flow-shop
scheduling problems," International Journal of Production Research, vol.
49, pp. 937-951, Sept. 2007.
[7] K. C. Tan, C. K. Goh, A. A. Mamun, and E. Z. Ei, "An evolutionary
artificial immune system for multi-objective optimization," European
Journal of Operational Research, vol. 187, pp. 371-392, Jun. 2008.
[8] T. Bagchi, Multiobjective Scheduling by Genetic Algorithms (Book
style). New York, 1999.
[9] C. R. Reeves, "A Genetic Algorithm for Flowshop Sequencing,"
Computers and Operations Research, vol. 5, pp. 5-13, Jan. 1995.
[10] J. D. Farmer, N. Packard, and A. Perelson, "The immune system,
adaptation and machine learning," Physica D, vol. 2, pp. 187-204,
Oct.-Nov. 1986.
[11] H. Bersini, and F. J. Varela, "Hints for adaptive problem solving gleaned
from immune networks," Parallel Problem Solving from Nature, vol. 496,
pp. 343-354, 1991.
[12] J. O. Kephart, "A biologically inspired immune system for computers,"
Proceedings of Artificial Life IV: The Fourth International Workshop on
the Synthesis and Simulation of Living Systems, MIT Press. pp. 130-139,
1994.
[13] D. Dasgupta, Artificial Immune Systems and Their Applications (Book
style). Berlin : Springer-Verlag, Jan. 1999.
[14] L. N. de Castro and F. J. Von Zuben, Artificial Immune Systems: Part I
-Basic Theory and Applications (Book style). Brazil, 1999.
[15] V. Cutello and G. Nicosia, "An Immunological Approach to
Combinatorial Optimization Problems," Lecture Notes in Computer
Science, vol. 2527, pp. 361-370, 2002.
[16] V. Cutello, G. Nicosia, M. Pavone, and J. Timmis, "An Immune
Algorithm for Protein Structure Prediction on Lattice Models," IEEE
Transactions on Evolutionary Computation, vol. 11 , pp. 101-117, 2007.
[17] J. Zhang, C. Zhang, and S. Liang, "The circular discrete particle swarm
optimization algorithm for flow shop scheduling problem," Expert
Systems with Applications, vol. 37, pp. 5827-5834, 2010.
[18] P. C. Chang, W. H. Huang, and C. J. Ting, "Self-evolving Artificial
Immune System via Developing T and B Cell for Permutation Flow-shop
Scheduling Problems," Proceedings of World Academy of Science,
Engineering and Technology, vol. 65, pp. 822-827, May 2010.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55539", author = "Meng-Hui Chen and Pei-Chann Chang and Wei-Hsiu Huang", title = "The Design of Self-evolving Artificial Immune System II for Permutation Flow-shop Problem", abstract = "Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.", keywords = "Artificial Immune System, Clonal Selection, Immune
Response, Permutation Flow-shop Scheduling Problems", volume = "6", number = "5", pages = "892-5", }
