Artificial Immune Memory: Perspectives on Internal Image with Antibody Dynamics

2012 ◽  
Author(s):  
Chung-Ming Ou ◽  
Chung-Jen Ou
Keyword(s):  
Immune Memory ◽  
Artificial Immune ◽  
Internal Image ◽  
Antibody Dynamics

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.

scholarly journals The Artificial Immune System Approach for Smart Air-Conditioning Control

2012 ◽  
Vol 58 (2) ◽  
pp. 193-199
Author(s):  
Zenon Chaczko ◽  
Shahrzad Aslanzadeh ◽  
Jonathan Kuleff
Keyword(s):  
Immune System ◽  
System Approach ◽  
Air Conditioning ◽  
Artificial Immune System ◽  
Immune Memory ◽  
Artificial Immune ◽  
Air Conditioning System ◽  
System Solution ◽  
The Impact ◽  
Conditioning Control

The Artificial Immune System Approach for Smart Air-Conditioning ControlBiologically inspired computing that looks to nature and biology for inspiration is a revolutionary change to our thinking about solving complex computational problems. It looks into nature and biology for inspiration rather than conventional approaches. The Human Immune System with its complex structure and the capability of performing pattern recognition, self-learning, immune-memory, generation of diversity, noise tolerance, variability, distributed detection and optimisation - is one area that has been of strong interest and inspiration for the last decade. An air conditioning system is one example where immune principles can be applied. This paper describes new computational technique called Artificial Immune System that is based on immune principles and refined for solving engineering problems. The presented system solution applies AIS algorithms to monitor environmental variables in order to determine how best to reach the desired temperature, learn usage patterns and predict usage needs. The aim of this paper is to explore the AIS-based artificial intelligence approach and its impact on energy efficiency. It will examine, if AIS algorithms can be integrated within a Smart Air Conditioning System as well as analyse the impact of such a solution.

scholarly journals A Hybrid Algorithm Between the Artificial Immunological Network and the ANFIS Technique with Application

2018 ◽  
Vol 2 (2) ◽  
pp. 315-329
Author(s):  
Omar Kasim ◽  
Israa Alkhayyat
Keyword(s):  
Hybrid Algorithm ◽  
Artificial Immune System ◽  
Fuzzy Inference ◽  
Original Data ◽  
Immune Memory ◽  
Immune Network ◽  
Artificial Immune ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Immune Network

the Artificial Immune Network, was developed using the Adaptive Neuro Fuzzy Inference System (ANFIS) by using the Immune Memory, The immunological network as an input to ANFIS technology to increase the chance of training a model based on the pattern of simulation of data patterns, as the immune memory resulting from the immunological network makes copies of data with characteristics similar to the original data. The proposed algorithm proved to have better and more efficient results rack on the patterns compared with the usual artificial immune system.

Research on Situation Evaluation Based on Artificial Immune for Network Security

2011 ◽  
Vol 121-126 ◽  
pp. 4926-4930
Author(s):  
Rui Rui Zhang ◽  
Xin Xiao
Keyword(s):  
Network Security ◽  
Large Scale ◽  
Evaluation Model ◽  
Research Area ◽  
Immune Memory ◽  
Artificial Immune ◽  
Uncertainty Reasoning ◽  
Large Scale Network ◽  
Concentration Changes ◽  
New Research

As a new research area of network security, network security situation evaluation is significant for achieving large-scale network security monitoring. In this paper, the artificial immune technology is applied to the study of situation evaluation for network security. Mathematical expressions of immune elements such as antibodies, antigens are established, and basic immune mechanism such as self-tolerance, clone selection, immune memory are achieved. According to the relationships between concentration changes of antibodies and attack intensity of pathogens in biological immune system, a situation evaluation model for network security is proposed. In addition, this paper adopts the uncertainty reasoning method in the cloud theory to make multi-granularity analysis for network security situation. By modeling the security situation indicator, and using cloud rules generator and reverse cloud generator, we can get qualitative results of hosts and network's security situation. Theoretical analysis and experimental results show that the model is effective to evaluate situation for network security with advantages of real-time, adaptability and high accuracy.

Study on Improved Multi-Objective Adaptive Artificial Immune Genetic Algorithm

2013 ◽  
Vol 459 ◽  
pp. 232-238
Author(s):  
Zhen Yun Hu ◽  
Wei Zhang ◽  
Chen Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Immune Memory ◽  
Test Functions ◽  
Artificial Immune ◽  
Immune Genetic Algorithm ◽  
Multi Objective ◽  
Crowding Distance ◽  
Different Dimensions ◽  
Reaction Speed

In this paper, the traditional NSGA model in solving multi-objective optimization problem exists the computational complexity, lack of elitism and the need to set shared radius etc. defects. we use the advantage of the artificial immune system, such as good generalization, self-organization and so on, propose a improved multi-objective adaptive artificial immune genetic algorithm that by the use of fast non-dominated sorting and crowding distance, reduces the algorithm complexity and improves its stability and versatility; utilizes the immune memory cells to optimize the population quality, accelerate the antibody reaction speed and raise the optimize efficiency; updates the populations by the non-inferior rank and crowding distance to improve the algorithm function of the search. This paper elaborates the algorithm steps in detail and verifies this algorithm. Through the three different dimensions of test functions, the simulation results show that this algorithm is effective and feasible.

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