Application of novel clonal algorithm in multiobjective optimization

International Journal of Information Technology and Decision Making

Volumn 9, Issue 2, 2010, Pages 239-266

  Chen, Jianyong ^a   Lin, Qiuzhen ^a   Hu, Qingbin ^a  

^a SHENZHEN UNIVERSITY   (China)

Author keywords

Clonal selection; Hybrid mutation; Immune algorithm; Multiobjective optimization

Indexed keywords

EID: 77951678668     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622010003804     Document Type: Article

References (34)

1. F. M. Yang, G. W. Hua, H. Inoue and J. M. Shi, Two bi-objective optimization models for competitive location problems, Int. J. Inf. Technol. Decis. Mak. 5 (3) (2006) 531-543.
2. C. Hu and S. Li, Enhanced interactive satisfying optimization approach to multiple objective optimization with preemptive priorities, Int. J. Inf. Technol. Decis. Mak. 5 (1) (2006) 47-63.
3. M. S. Arumugam and M. V. C. Rao, On a class of hybrid systems via a novel approach for real-coded genetic algorithm with hybrid selection, Int. J. Inf. Technol. Decis. Mak. 6 (2) (2007) 315-332.
4. E. C. Brown, C. T. Ragsdale and A. E. Carter, A grouping genetic algorithm for the multiple traveling salesperson problem, Int. J. Inf. Technol. Decis. Mak. 6 (2007) 333-347.
5. C. M. Fonseca and P. J. Fleming, An overview of evolutionary algorithms in multi-objective optimization, Evol. Comput. 1 (1995) 1-16.
6. J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in Proc. First Int. Conf. Genetic Algorithms (Lawrence Erlbaum Associates, 1985), pp. 93-100.
7. P. Hajela and C. Y. Lin, Genetic search strategies in multicriterion optimal design, Struct. Multidiscip. Optim. 4 (1992) 99-107.
8. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002) 182-197.
9. E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, in Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (Athens, Greece, 2002), pp. 95-100.
10. J. D. Knowles and D. W. Corne, Approximating the non-dominated front using the pareto archived evolution strategy, Evol. Comput. 8 (2) (2000) 149-172.
11. L. N. De Castro and F. J. Von Zuben, Learning and optimization using the clonal selection, IEEE Trans. Evol. Comput. 6 (3) (2002) 239-251.
12. P. K. Harmer, P. D. Williams, G. H. Gunsch and G. B. Lamont, An artificial immune system architecture for computer security applications, IEEE Trans. Evol. Comput. 6 (3) (2002) 252-280.
13. Hong-Wei Ge, Liang Sun, Yan-Chun Liang and Feng Qian, An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling systems, IEEE Trans. Man Cybern. 38 (2) (2008) 358-368.
14. A. A. Freitas and J. Timmis, Revisiting the foundations of artificial immune systems for data mining, IEEE Trans. Evol. Comput. 11 (4) (2007) 521-540.
15. Y. Peng, G. Kou, Y. Shi and Z. X. Chen, A descriptive framework for the field of data mining and knowledge discovery, Int. J. Inf. Technol. Decis. Mak. 7 (4) (2008) 639-682.
16. H. F. Wang and Z. W. Chan, A pruning approach to pattern discovery, Int. J. Inf. Technol. Decis. Mak. 7 (4) (2008) 721-736.
17. B. Ramaseshan, N. R. Achuthan and R. Collinson, Decision support tool for retail shelf space optimization, Int. J. Inf. Technol. Decis. Mak. 7 (3) (2008) 547-565.
18. C. A. Coello Coello and N. C. Cortes, Solving multi-objective optimization problems using an artificial immune system, Genet. Program. Evolvable Mach. 6 (2005) 163-190.
19. F. Freschi and M. Repetto, Multi-objective optimization by a modified artificial immune system algorithm, in Proc. 4th Int. Conf. Artificial Immune Systems 3627 of Lect. Notes Comput. Sci. (ICARIS, 2005), pp. 248-261.
20. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefen Bo, Multi-objective immune algorithm with nondominated neighbor-based selection, Evol. Comput. 16 (MIT Press, 2008) 225-255.
21. R. C. Purshouse and P. J. Fleming, on the evolutionary optimization of many conflicting objectives, IEEE Trans. Evol. Comput. 11 (6) (2007) 770-784.
22. D. W. Corne, J. D. Knowles and M. J. Oates, The pareto-envelope based selection algorithm for multi-objective optimization, in Proc. 6th Int. Conf. Parallel Problem Solving from Nature (PPSN VI), (2000), pp. 839-848.
23. S. Bandyopadhyay, S. Saha, U. Maulik and K. Deb, A simulated annealing-based multiobjective optimization algorithm: AMOSA, IEEE Trans. Evol. Comput. 12 (3) (2008) 269-283.
24. Z. Bingul, Adaptive genetic algorithms applied to dynamic multiobjective problems, Appl. Soft Comput. 7 (3) (2007) 791-799. (Pubitemid 46551913)
25. D. A. Van Veldhuizen and G. B. Lamont, On measuring multiobjective evolutionary algorithm performance, in Proc. 2000 Congr. Evol. Comput. Vol. 1 (2000) pp. 204-211.
26. Chen Min-Rong and Lu Yong-Zai, A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization, Eur. J. Oper. Res. 188 (3) (2008) 637-651. (Pubitemid 351173470)
27. K. Deb and H. G. Beyer, Self-adaptive genetic algorithms with simulated binary crossover, Evol. Comput. 9 (2) (2001) 197-221.
28. Nitesh Khilwani, Anoop Prakash, Ravi Shankar and M. K. Tiwar, Fast clonal algorithm, Eng. Appl. of Artif. Intell. 21 (1) (2008) 106-128. (Pubitemid 350117449)
29. E. Zitzler, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results, Evol. Comput. 8 (2) (Summer 2000) 173-195.
30. K. Deb, L. Thiele, M. Laumanns and E. Zitzler, Scalable multi-objective optimization test problems, Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) (Zurich, Switzerland, 2001).
31. E. Zitzler and L. Thiele, Multi-objective optimization using evolutionary algorithms - A comparative case study, in Parallel Problem Solving from Nature, volume 1498 of Lecture Notes in Computer Science (1998), pp. 292-301.
32. J. R. Schott, Fault tolerant design using single and multictiteria genetic algorithm optimization, Master thesis, Massachusetts Institute of Technology (1995).
33. P. A. N. Bosman and D. Thierens, The balance between proximity and diversity in multiobjective evolutionary algorithms, IEEE Trans. Evol. Comput. 7 (2) (2003) 174-188.
34. R. McGill, J. Tukey and W. Larsen, Variations of box plots, Am. Stat. 32 (1978) 12-16.