The Quaternion Model of Artificial Immune Response

2005 ◽  
pp. 207-219 ◽  
Author(s):  
Maoguo Gong ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Haifeng Du
Keyword(s):  
Immune Response ◽  
Artificial Immune

An Artificial Immune Dynamical System for Optimization

2011 ◽  
pp. 49-75
Author(s):  
Licheng Jiao ◽  
Maoguo Gong ◽  
Wenping Ma
Keyword(s):  
Dynamical System ◽  
Immune Response ◽  
Numerical Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Immune Memory ◽  
Artificial Immune ◽  
Dynamic Algorithm ◽  
Whole Process

Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection, positive selection, rather than the whole process of immune response to solve computational problems. In order to build a general computational framework by simulating immune response process, this chapter introduces a population-based artificial immune dynamical system, termed as PAIS, and applies it to numerical optimization problems. PAIS models the dynamic process of human immune response as a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. Some general descriptions of reaction rules, including the set of clonal selection rules and the set of immune memory rules are introduced in PAIS. Based on these reaction rules, a dynamic algorithm, termed as PAISA, is designed for numerical optimization. In order to validate the performance of PAISA, 9 benchmark functions with 20 to 10,000 dimensions and a practical optimization problem, optimal approximation of linear systems are solved by PAISA, successively. The experimental results indicate that PAISA has high performance in optimizing some benchmark functions and practical optimization problems.

An Artificial Immune System for Frequency-Variable Air Conditioner

2010 ◽  
Vol 34-35 ◽  
pp. 1449-1452
Author(s):  
Xue Peng Liu ◽  
Dong Mei Zhao ◽  
Bin Wang
Keyword(s):  
Immune Response ◽  
Immune System ◽  
Pid Controller ◽  
Artificial Immune System ◽  
Stable Condition ◽  
Antibody Concentration ◽  
Air Conditioner ◽  
Artificial Immune ◽  
Good Convergence ◽  
Abnormal Behaviors

It is common to control the Frequency-variable air conditioner (A/C) by using PID controller. However, an arithmetic based on artificial immune system was proposed. The immune system of organism was analyzed, and an architecture of the arithmetic was designed. The A/C behaviors were expressed by antibodies, a concentration model of antibody was built, and rules of A/C behaviors could be obtained by the antibody concentration. the initial immune response arithmetic and the secondary immune response arithmetic were designed, which were used to memorized normal behaviors and detect abnormal behaviors. Experiments show that the scheme is capable of adapting to system variation. The system can obtain the stable condition with good convergence even high temperature of 45°C

A FUZZY INFERENCE SYSTEM TO DETERMINE THE NUMBER OF CLONES IN A CLASS OF ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 11 (01) ◽  
pp. 1250005
Author(s):  
LUIZ ANTONIO CARRARO ◽  
LEANDRO NUNES DE CASTRO ◽  
ANGELITA MARIA DE RE ◽  
FABRĹCIO OLIVETTI DE FRANÇA
Keyword(s):  
Immune Response ◽  
Immune Cell ◽  
Fuzzy Inference ◽  
Clonal Selection ◽  
Selection Process ◽  
Artificial Immune Systems ◽  
Reproduction Rate ◽  
Artificial Immune ◽  
Inference System ◽  
Immune Systems

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

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