Large-scale optimization using immune algorithm

Immune-inspired optimization algorithms encoded the parameters into individuals where each individual represents a search point in the space of potential solutions. A large number of parameters would result in a large search space. Nowadays, there is little report about immune algorithms effectively solving numerical optimization problems with more than 100 parameters. In this paper, we introduce an improved immune algorithm, termed as Dual-Population Immune Algorithm (DPIA), to solve large-scale optimization problems. DPIA adopts two side-by-side populations, antibody population and memory population. The antibody population employs the cloning, affinity maturation, and selection operators, which emphasizes the global search. The memory population stores current representative antibodies and the update of the memory population pay more attention to maintain the population diversity. Normalized decimal-string representation makes DPIA more suitable for solving large-scale optimization problems. Special mutation and recombination methods are adopted to simulate the somatic mutation and receptor editing process. Experimental results on eight benchmark problems show that DPIA is effective to solve large-scale numerical optimization problems.

[1]  D. Dasgupta,et al.  A formal model of an artificial immune system. , 2000, Bio Systems.

[2]  Utpal Garain,et al.  Recognition of Handwritten Indic Script Using Clonal Selection Algorithm , 2006, ICARIS.

[3]  Jonathan Timmis,et al.  Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation , 2003, GECCO.

[4]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[6]  T. Fukuda,et al.  Immune Networks Using Genetic Algorithm for Adaptive Production Scheduling , 1993 .

[7]  C. Berek,et al.  The maturation of the immune response. , 1993, Immunology today.

[8]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[9]  Fang Liu,et al.  The Quaternion Model of Artificial Immune Response , 2005, ICARIS.

[10]  Maoguo Gong,et al.  Solving multidimensional knapsack problems by an immune-inspired algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[11]  A. George,et al.  Receptor editing during affinity maturation. , 1999, Immunology today.

[12]  Yoshiteru Ishida Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[13]  D. Dasgupta,et al.  Combining negative selection and classification techniques for anomaly detection , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[15]  H.,et al.  The Immune System as a Model for Pattern Recognition and Classification , 1999 .

[16]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[17]  Shinn-Ying Ho,et al.  Intelligent evolutionary algorithms for large parameter optimization problems , 2004, IEEE Trans. Evol. Comput..

[18]  Vincenzo Cutello,et al.  Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators , 2004, ICARIS.

[19]  J Timmis,et al.  An artificial immune system for data analysis. , 2000, Bio Systems.

[20]  F. Azuaje Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[21]  Simon M. Garrett Parameter-free, adaptive clonal selection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).