Improved immune algorithm for global numerical optimization and job-shop scheduling problems

Applied Mathematics and Computation

Volumn 194, Issue 2, 2007, Pages 406-424

  Tsai, Jinn Tsong ^a   Ho, Wen Hsien ^b   Liu, Tung Kuan ^c   Chou, Jyh Horng ^c  

^a NATIONAL PINGTUNG UNIVERSITY   (Taiwan)

^b KAOHSIUNG MEDICAL UNIVERSITY   (Taiwan)

^c NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

Author keywords

Immune algorithm; Job shop scheduling problems; Numerical optimization problems; Taguchi method

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; OPTIMIZATION;

HYPERMUTATION; IMMUNE ALGORITHMS; JOB-SHOP SCHEDULING PROBLEMS; NUMERICAL OPTIMIZATION PROBLEMS; TAGUCHI METHOD;

NUMERICAL ANALYSIS;

EID: 36049016444     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2007.04.038     Document Type: Article

References (48)

1. Angelov P. Supplementary crossover operator for genetic algorithms based on the center-of-gravity paradigm. Control and Cybernetics 30 (2001) 159-176
2. Bazaraa M., Jarvis J., and Sherali H. Linear Programming and Network Flows (1990), John Wiley and Sons, New York
3. Chellapilla K. Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation 2 (1998) 91-96
4. Chen J., and Xu L. A dynamic Niche genetic algorithm for multimodal function optimization. Journal of Tongji University 34 (2006) 684-688
5. X.P. Chen, B. Qu, G. Lu, An application of immune algorithm in FIR filter design, in: Proceedings of the 2003 IEEE International Conference on Neural Network and Signal Processing, Nanjing, 2003, pp. 473-477.
6. Cheng R., Gen M., and Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms: Part I representation. Computers and Industrial Engineering 30 (1996) 983-997
7. J.H. Chou, W.H. Liao, J.J. Li, Application of Taguchi-genetic method to design optimal Grey-Fuzzy controller of a constant turning force system, in: Proceedings of the 15th CSME Annual Conference, Taiwan, 1998, pp. 31-38.
8. Dai X.M., Chen Z.G., Feng R., Mao X.F., and Shao H.H. Improved algorithm of pattern extraction based mutation approach to genetic algorithm. Journal of Shanghai Jiaotong University 36 (2002) 1158-1160
9. L. Davis, Adapting operator probabilities in genetic algorithms, in: Proceedings of the 1989 International Conference on Genetic Algorithms, San Mateo, 1989, pp. 61-69.
10. de Castro L.N., and Von Zuben F.J. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6 (2002) 239-251
11. L.N. de Castro, J. Timmis, An Artificial Immune Network for Multimodal Function Optimization, in: Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, 2002, pp. 699-704.
12. de Castro L.N., and Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach (2002), Springer-Verlag, London
13. H. Ge, Z.Y. Mao, Immune algorithm, in: Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, 2002, pp. 1784-1788.
14. Gen M., and Cheng R. Genetic Algorithms and Engineering Design (1997), John Wiley and Sons, New York
15. M. Gen, Y. Tsujimura, E. Kubota, Solving job-shop scheduling problems by genetic algorithm, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Texas, 1994, pp. 1577-1582.
16. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley, Massachusetts
17. Grefenstette J.J. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on System, Man, and Cybernetics 16 (1986) 122-128
18. Harris R.S., Kong Q., and Maizels N. Somatic hypermutation and the three R's: repair, replication and recombination. Mutation Research 436 (1999) 157-178
19. Hsieh C.H., Chou J.H., and Wu Y.J. Optimal predicted fuzzy controller of a constant turning force systems with fixed metal removal rate. Journal of Materials Processing Technology 123 (2002) 22-30
20. Hwang S.F., and He R.S. A hybrid real-parameter genetic algorithm for function optimization. Advanced Engineering Informatics 20 (2006) 7-21
21. Jerne N.K. The immune system. Scientific American 229 (1973) 52-60
22. Kim G.H., and Lee C.S.G. Genetic reinforcement learning approach to the heterogeneous machine scheduling problem. IEEE Transactions on Robotics and Automation 14 (1998) 879-893
23. Leung Y.W., and Wang Y. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5 (2001) 41-53
24. Li G., and Li J.Y. On job-shop scheduling by hybrid genetic algorithm. Journal of Tianjin University of Science and Technology 36 (2003) 239-242
25. Liu T.K., Tsai J.T., and Chou J.H. Improved genetic algorithm for job-shop scheduling problem. International Journal of Advanced Manufacturing Technology 27 (2006) 1021-1029
26. Luh G.C., Chueh C.H., and Liu W.W. MOIA: multi-objective immune algorithm. Engineering Optimization 35 (2003) 143-164
27. Luh G.C., and Chueh C.H. Multi-objective optimal design of truss structure with immune algorithm. Computers and Structure 82 (2004) 829-844
28. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs (1996), Springer-Verlag, Berlin
29. Montgomery D.C. Design and Analysis of Experiments (1991), Wiley, New York
30. I. Ono, M. Yamamura, S. Kobayashi, A genetic algorithm for job-shop scheduling problem using job-based order crossover, in: Proceedings of the IEEE International Conference on Evolutionary Computation, Nagoya, 1996, pp. 547-552.
31. Park S.H. Robust Design and Analysis for Quality Engineering (1996), Chapman & Hall, London
32. Phadke M.S. Quality Engineering Using Robust Design (1989), Prentice-Hall, New Jersey
33. J. Renders, H. Bersini, Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways, in: Proceedings of the First IEEE Conference on Evolutionary Computation, Florida, 1994, pp. 312-317.
34. Ross P.J. Taguchi Techniques for Quality Engineering (1989), McGraw-Hill, Singapore
35. Shi G. A genetic algorithm applied to a classic job-shop scheduling problem. International Journal of Systems Science 28 (1997) 25-32
36. Shi Y.J., Teng H.F., and Li Z.Q. Cooperative co-evolutionary differential evolution for function optimization. Lecture Notes in Computer Science 3611 (2005) 1080-1088
37. Taguchi G., Chowdhury S., and Taguchi S. Robust Engineering (2000), McGraw-Hill, New York
38. Tsai J.T., Liu T.K., and Chou J.H. Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8 (2004) 365-377
39. Tsai J.T., and Chou J.H. Design of optimal digital IIR filters by using an improved immune algorithm. IEEE Transactions on Signal Processing 54 12 (2006) 4582-4596
40. Tsujimura Y., Gen M., and Cheng R. Improved genetic algorithms for solving job-shop scheduling problem. Engineering Design and Automation 3 (1997) 133-144
41. Y. Tsujimura, T. Sugimoto, Y. Mafune, M. Gen, A genetic algorithm for job-shop scheduling by means of symbiosis mechanism, in: Proceedings of the 3rd Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems, Canberra, 1999, pp. 288-291.
42. Y. Tsujimura, Y. Mafune, M. Gen, Effects of symbiotic evolution in genetic algorithms for job-shop scheduling, in: Proceedings of the IEEE 34th International Conference on System Sciences, Hawaii, 2001, pp. 1-7.
43. I. Tazawa, S. Koakutsu, H. Hirata, An immunity based genetic algorithm and its application to the VLSI floorplan design problem, in: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, Nagoya, 1996, pp. 417-421.
44. Wang L. Intelligent Optimization Algorithms with Applications (2001), Tsinghua University Press, Beijing
45. Wang L., and Zheng D.Z. An effective hybrid optimization strategy for job-shop scheduling problems. Computers and Operations Research 28 (2001) 585-596
46. Wang L., and Zheng D.Z. A modified genetic algorithm for job-shop scheduling. International Journal of Advanced Manufacturing Technology 20 (2002) 72-76
47. Wu Y. Taguchi Methods for Robust Design (2000), The American Society of Mechanical Engineers, New York
48. J. Yen, B. Lee, A simplex genetic algorithm hybrid, in: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, Indiana, 1997, pp. 175-180.