Artificial Immune System for Solving Global Optimization Problems

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.

[1]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  Fabrício Olivetti de França,et al.  An artificial immune network for multimodal function optimization on dynamic environments , 2005, GECCO.

[3]  Jonathan Timmis,et al.  Chasing chaos , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[4]  Vincenzo Cutello,et al.  An Immunological Algorithm for Global Numerical Optimization , 2005, Artificial Evolution.

[5]  Jonathan Timmis,et al.  A resource limited artificial immune system for data analysis , 2001, Knowl. Based Syst..

[6]  Vincenzo Cutello,et al.  How to Escape Traps Using Clonal Selection Algorithms , 2004, ICINCO.

[7]  Jonathan Timmis,et al.  Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier , 2004, ICARIS.

[8]  Vincenzo Cutello,et al.  The Clonal Selection Principle for In Silico and In Vitro Computing , 2005 .

[9]  Andrew Hone,et al.  Optima, Extrema, and Artificial Immune Systems , 2004, ICARIS.

[10]  Vincenzo Cutello,et al.  An Immunological Approach to Combinatorial Optimization Problems , 2002, IBERAMIA.

[11]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Dipankar Dasgupta,et al.  Immunological Computation: Theory and Applications , 2008 .

[13]  Bijaya K. Panigrahi,et al.  A micro-bacterial foraging algorithm for high-dimensional optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[14]  Maoguo Gong,et al.  Improved real-valued clonal selection algorithm based on a novel mutation method , 2007, 2007 International Symposium on Intelligent Signal Processing and Communication Systems.

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

[16]  Jon Timmis,et al.  Artificial Immune Recognition System (AIRS): Revisions and Refinements , 2002 .

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

[18]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

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

[20]  Kumar Chellapilla,et al.  Combining mutation operators in evolutionary programming , 1998, IEEE Trans. Evol. Comput..

[21]  Li Liu,et al.  A cooperative artificial immune network with particle swarm behavior for multimodal function optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[22]  Maoguo Gong,et al.  Improved Clonal Selection Algorithm based on Lamarckian local search technique , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[23]  Senhua Yu,et al.  MILA - Multilevel Immune Learning Algorithm , 2003, GECCO.

[24]  Jason Brownlee,et al.  Clonal selection algorithms , 2007 .

[25]  Vincenzo Cutello,et al.  Immune Algorithms with Aging Operators for the String Folding Problem and the Protein Folding Problem , 2005, EvoCOP.

[26]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

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

[28]  N. K. Jerne,et al.  Clonal selection in a lymphocyte network. , 1974, Society of General Physiologists series.

[29]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[30]  Yidan Luo,et al.  An Improved Clonal Selection Algorithm and Its Application in Function Optimization Problems , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[31]  Jerne Nk Towards a network theory of the immune system. , 1974 .

[32]  Jonathan Timmis,et al.  Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[33]  Vincenzo Cutello,et al.  Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator , 2006, SAC.