Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithm
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- 作者：Wilburn W. P. Tsang (1)
Henry Y. K. Lau (1) hyklau@hku.hk - 关键词：Artificial immune systems – ; Evolutionary Algorithm – ; Multi ; objective optimization
- 刊名：Lecture Notes in Computer Science
- 出版年：2012
- 出版时间：2012
- 年：2012
- 卷：7597
- 期：1
- 页码：72-85
- 全文大小：247.7 KB
- 参考文献：1. Watkins, A., Timmis, J.: Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 427–438. Springer, Heidelberg (2004)
2. Timmis, J.: Artificial immune systems - today and tomorrow. Natural Computing 6, 1–18 (2007)
3. Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Berlin (2006)
4. Tan, K.C., Goh, C.K., Mamun, A.A., Ei, E.Z.: An evolutionary artificial immune system for multi-objective optimization. European Journal of Operational Research 187, 371–392 (2008)
5. Timmis, J., Andrews, P., Owens, N., Clark, E.: An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence 1, 5–26 (2008)
6. Roitt, I., Brostoff, J., Male, D.: Immunolohy, 6th edn., Mosby (2001)
7. Satthaporn, S., Eremin, O.: Dendritic cells (I): biological functions. J. R. Coll. Surg. Edinb. 46, 9–19 (2001)
8. Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press (1959)
9. Jerne, N.K.: Towards a Network Theory of the Immune System. Annual Immunolgy 125(C), 373–389 (1974)
10. Dasgupta, D., Ji, Z., Gonzalez, F.: Artificial immune system (AIS) research in the last five years. In: IEEE Congress on Evolutionary Computation 2003 (CEC 2003), pp. 123–130. IEEE (2003)
11. Matzinger, P.: The danger model: a renewed sense of self. Science 296, 301–305 (2002)
12. Greensmith, J., Aickelin, U., Cayzer, S.: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)
13. Kim, J., Bentley, P.J.: The Human Immune system and Network Intrusion Detection. In: 7th European Congress on Intelligent Techniques and Soft Computing, EUFIT 1999 (1999)
14. Lau, H.Y.K., Wong, V.W.K.: A strategic behavior-based intelligent transport system with artificial immune system. In: Proc. of IEEE International Conference on Systems, Man and Cybernetics, pp. 3909–3914. Springer (2004)
15. Lau, H.Y.K., Tsang, W.W.P.: A Parallel Immune Optimization Algorithm for Numeric Function Optimization. Evolutionary Intelligence 1, 171–185 (2008)
16. 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: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 54–63. Springer, Heidelberg (2005)
17. Coello Coello, C.A., Cort茅s, N.C.: An approach to solve multiobjective optimization problems based on an artificial immune system. In: Timmis, J., Bentley, P.J. (eds.) Proc. of the First International Conference on Artificial Immune Systems (ICARIS 2002), pp. 212–221 (2002)
18. Coello Coello, C.A., Cort茅s, N.C.: Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines 6, 163–190 (2005)
19. Luh, G.-C., Chueh, C.-H., Liu, W.-W.: MOIA: multi-objective immune algorithm. Engineering Optimization 35, 143–164 (2003)
20. Freschi, F., Repetto, M.: Multiobjective Optimization by a Modified Artificial Immune System Algorithm. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 248–261. Springer, Heidelberg (2005)
21. Gong, M., Jiao, L., Du, H., Bo, L.: Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection. Evolutionary Computation 16, 225–255 (2008)
22. Tsang, W.W.P., Lau, H.Y.K.: Enhanced Network Interaction in Multi-Objective Immune Optimization Algorithm. In: 8th International Conference on Optimization: Techniques and Applications (ICOTA8), Shanghai, China (2010)
23. Knowles, J.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In: Proc. of the 1999 Congress on Evolutionary Computation (CEC 1999), pp. 98–105. IEEE (1999)
24. Corne, D.W., Jerram, N.R., Knowles, J., Oates, M.J.: PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 283–290. Morgan Kaufmann (2001)
25. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Company, Boston (1989)
26. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
27. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Swiss Federal Institute of Technology (2001)
28. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proc. of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 825–830. IEEE (2002)
29. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 8, 173–195 (2000)
30. Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7, 174–188 (2003)
31. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)
32. Fleischer, M.: The Measure of Pareto Optima Applications to Multi-objective Metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)
33. Gong, M.: NNIA Toolbox Version 1.0 (2006), http://see.xidian.edu.cn/iiip/mggong/Projects/NNIA.html
34. Nebro, A.J., Durillo, J.J.: jMetal (Metaheuristic Algorithms in Java) Version 1.5. Sourceforge.net (2008) - 作者单位：1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong
- ISSN：1611-3349
文摘
In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods – artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms.