International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.4, No.6, Jun. 2012
A General Framework for Multi-Objective Optimization Immune Algorithms
Full Text (PDF, 3851KB), PP.1-13
Artificial Immune System (AIS) is a hotspot in the area of Computational Intelligence. While the Multi-Objective Optimization (MOP) problem is one of the most widely applied NP-Complete problems. During the past decade more than ten kinds of Multi-Objective optimization algorithms based on AIS were proposed and showed outstanding abilities in solving this kind of problem. The paper presents a general framework of Multi-Objective Immune Algorithms, which summarizes a uniform outline of this kind of algorithms and gives a description of its principles, mainly used operators and processing methods. Then we implement the proposed framework and build four typical immune algorithms on it: CLONALG, MISA, NNIA and CMOIA. The experiment results showed the framework is very suitable to develop the various multi-objective optimization immune algorithms.
Cite This Paper
Chen Yunfang,"A General Framework for Multi-Objective Optimization Immune Algorithms", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.6, pp.1-13, 2012. DOI: 10.5815/ijisa.2012.06.01
Serafini, P. Some considerations about computational complexity for multi-objective combinatorial problems LNEMS, Vol. 294, Springer , p222-232, 1987.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
Q. Zhang and H. Li, MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transaction Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007.
Knowles, J. D. & Corne, D. W.: The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimization. In Proc. of 1999 Congress on Evolutionary Computation, vol. 1,pp. 98–105. Piscataway, NJ: IEEE Press, 1999.
HC. Lau, W. Wang. A Multi-Agent Approach for Solving Optimization Problems involving Expensive Resources, In Proc of 2005 ACM Symposium on Applied Computing: 79-83, 2005.
Farmer, J. D., N. H. Packard and A. Perelson. The Immune System, Adaptation, and Machine Learning. Physica D 22(1-3): 187-204, 1986.
J. Zheng, Y. Chen, W. Zhang. A Survey of Artificial Immune Applications. Artificial Intelligence Review 43:19-34, 2010.
Jerne, N. K.: Towards a Network Theory of the Immune System. Ann. Immunology, Vol. 125C, 373-389, 1974.
P. Hajela and J. Lee. Constrained genetic search via schema adaption: An immune network. Structural and Multidisciplinary Optimization, Vol. 12, No. 1: 11-159, 1996.
J.J. Durillo and A.J. Nebro and E. Alba.The jMetal Framework for Multi-Objective Optimization: Design and Architecture. In Proc of 2010 Conference on Evolutionary Computation: 4138-4325, 2010.
L.N. de Castro and F.J. Von Zuben. Learning and Optimization Using the Clonal Selection Principle, IEEE Transaction Evolutionary Computation, Vol. 6, No. 3:239-251, 2002
M. Gong, L. Jiao et al.. Multiobjective Immune Algorithm with Nondominated Neighbor-based Selection, Evolutionary Computation Vol.16, No.2: 225–255, 2008.
C. Coello and N.C. Cortes. Solving Multiobjective Optimization Problems using an Artiﬁcial Immune System, Genetic Programming and Evolvable Machines, 6:163–190, 2002.
F. Sun, Y. Chen, W. Wu. Multi-objective Optimization Immune Algorithm Using Clustering. In Proc of 2010 International Conference of Bio-Inspired System and Signal Processing, IEEE: 9-13, 2010.
S. Forrest, A.S. Perelson et al.. Self-Nonself Discrimination in a Computer. In Proc of 1994 IEEE Symposium on Research in Security and Privacy: 202-212, 1994.
Garrett, S.M., 2004, Parameter-free, adaptive clonal selection, In Proc of Congress on Evolutionary Computing, Portland Oregon, USA, Volume 1, pp. 1052-1058, 2004.
L.N de Castro and Timmis. An artificial immune network for multimodal function optimization. In proceedings of the IEEE Congress on Evolutionary Computation Honolulu :699-704, 2002.
Freschi, F., Repetto, M. Multiobjective optimization by a modified artificial immune system algorithm. 4th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science 3627:248-261, 2005.
Luh, G.C., Chueh, C. H., and Liu W.W.. MOIA: Multi-objective Immune Algorithm. Engineering Optimization, 35 (2): pp. 143-164, 2003.
Wong, E.Y.C., Yeung, H.S.C., and Lau, H.Y.K. Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning, Journal of Engineering Applications of Artificial Intelligence, Vol. 22 Issue 6: 842-854, 2009.
Wang, X.L., Mahfouf, M.. ACSAMO: an adaptive multi-objective optimization algorithm using the clonal selection principle. In Proc of 2nd European Symposium on Nature inspired Smart Information Systems, 2006.
Knowles, J. D. & Corne, D. W.: The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimization. In Proc. of 1999 Congress on Evolutionary Computation (ed. P. J. Angeline), vol. 1,pp. 98–105. Piscataway, NJ: IEEE Press. 1999.
Knowles, Joshua D. and David W. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.
Hamming, Richard W.. Error detecting and error correcting codes, Bell System Technical Journal 29 (2):147-160, 1950.