Artificial Immune Theory Research Articles

Research on the characteristics of the growth risk sources of new ventures based on Immune Theory

Under the background of the national strategy of “mass innovation and entrepreneurship” and the information age, it is very important to use the artificial immune theory to characterize the growth risk sources of new ventures and make them become the quantitative index features available for the model. It can lay the foundation for the construction of risk identification model of new ventures. First of all, through the in-depth analysis of the characteristics of the growth risk of the new venture, the source of the growth risk is defined according to the classification of the growth risk of the new venture; then, through combing and summarizing the relevant research at home and abroad, this paper analyzes the influencing factors of R & D risk source, product risk source, management risk source and social network risk source, verifies and selects through empirical research, and constructs the identification index system on this basis; finally, the growth risk characteristic structure of new ventures based on artificial immunity is constructed, and risk identification indicators are engineered to transform qualitative indicators into quantitative indicators for model experiments. It provides a basic reference for the establishment of the new enterprise growth risk identification model, so as to effectively meet the in-depth decision support, information mining and discovery in the growth process of the new enterprise, thereby promoting the stable and healthy growth of the new enterprise.

5G Message Service System Based on Artificial Immune Dynamic Adaptive Mechanism

When facing massive interconnection scenarios of terminals, 5G message service system needs to support large-scale information access and management and provides the efficient and reliable signal control to scheduling a variety of wireless access networks. In this paper, artificial immune theory is applied to the 5G message services. The simulating the immune response mechanism, a semi-distributed dynamic adaptive immune network architecture was proposed. The dynamic learning mechanism and immune memory mechanism of the detector were established, which improved the 5G edge computing capability and reduced the load of the 5G core network. The concept of the immune message distribution system (IMDS) is proposed, which uses clonal selection algorithm to classify and clone the message header, and combines with the affirmative selection algorithm to carry out high-frequency variation on the message body. On the premise of ensuring the diversity of antibodies, the space consumption problem of the hash-map algorithm is effectively solved, and the efficiency of message distribution is improved. The comparison of simulation results shows that the IMDS improves the message recognition ability and adaptive ability of the 5G system and proves the feasibility of semi-distributed immune dynamic learning mechanism.

Multi-objective constraint task scheduling algorithm for multi-core processors

A task scheduling algorithm is an effective means to ensure multi-core processor system efficiency. This paper defines the task scheduling problem for multi-core processors and proposes a multi-objective constraint task scheduling algorithm based on artificial immune theory (MOCTS-AI). The MOCTS-AI uses vaccine extraction and vaccination to add prior knowledge to the problem and performs vaccine selection and population updating based on the Pareto optimum, thereby accelerating the convergence of the algorithm. In the MOCTS-AI, the crossover and mutation operators and the corresponding use probability for the task scheduling problem are designed to guarantee both the global and local search ability of the algorithm. Additionally, the antibody concentration in the the MOCTS-AI is designed based on the bivariate entropy. By designing the selection probability in consideration of the concentration probability and fitness probability, antibodies with high fitness and low concentration are selected, thereby optimizing the population and ensuring its diversity. A simulation experiment was performed to analyze the convergence of the algorithm and the solution diversity. Compared with other algorithms, the MOCTS-AI effectively optimizes the scheduling length, system energy consumption and system utilization.

A new model of vehicular ad hoc networks based on artificial immune theory

Vehicular ad hoc networks (VANETs) are highly mobile and wireless networks intended to aid vehicular safety and traffic monitoring. To achieve these goals, we propose a VANET model based on immune network theory. Our model outperforms the delay tolerant mobility sensor network (DTMSN) model over a range of node numbers in terms of data packet arrival delay, arrival ratio, and throughput. These findings held true for the on-demand distance vector and connection-based restricted forwarding routing protocols. The model performed satisfactorily on a real road network.

A new model of vehicular ad hoc networks based on artificial immune theory

Vehicular ad hoc networks (VANETs) are highly mobile and wireless networks intended to aid vehicular safety and traffic monitoring. To achieve these goals, we propose a VANET model based on immune network theory. Our model outperforms the delay tolerant mobility sensor network (DTMSN) model over a range of node numbers in terms of data packet arrival delay, arrival ratio, and throughput. These findings held true for the on-demand distance vector and connection-based restricted forwarding routing protocols. The model performed satisfactorily on a real road network.

DETECTION OF INFORMATION SECURITY INCIDENTS BY THE MODIFIED ALGORITHM OF ARTIFICIAL IMMUNE SYSTEM WITH CLONAL SELECTION

In the article proposed the use of artificial immune systems as a heuristic detection method of information security incidents for algorithmic maintenance of intrusion detection systems. Describes the most known computational models of immune systems. As the basis of artificial immune system clonal selection theory is chosen. The algorithm of clonal selection is modified by use of the pseudorandom numbers generator on the basis of Blum- Blum- Shub’s algorithm. For affinity calculation in work the metrics coordination percent is used. Empirical results of an assessment of efficiency of the modified algorithm are received at approbation on a set of test data according to the procedure of research and their analysis. Conclusions about efficiency of application of the artificial immune system modified algorithm with clonal selection are formulated at the solution of a problem of deliberate changes detection on a controlled data set.

Modeling and Simulating of PMSM Space Vector PWM Drive System Based on Fuzzy Immune PI Control Method

In order to improve the speed control performance of the PMSM(Permanent magnet synchronous motor) vector control system. Utilizing the mathematical model of the PMSM in the dq0 coordinates, and the basic principles of the SVPWM (Space Vector Pulse Width Modulation) control method, a simulation model of the PMSM Space Vector Control System, which contains the double loop of speed and current, has been constructed based on the MATLAB/SIMULINK simulation environment. According to the artificial immune feedback theory and the approximation of the nonlinear function in the immune feedback system, with the help of fuzzy logic, a fuzzy immune PI controller has been made and harnessed in the speed loop. Under the three conditions: steady load startup, speed abrupt change, and load abrupt change, a series of contradistinctive simulation experiments are conducted and compared with the original PI controller respectively. The experimental results showed that the SVPWM control method with the fuzzy immune PI controller can not only make the motor run steadily, but also give it a superior speed performance and torque control characteristics. In addition, the control performance of the fuzzy immune PI controller showed a dramatically improvement compared with that of the traditional PI controller.

Demsptere-Shafer Evidence Theory in Enterprise Immunity Evaluation

Enterprise’ immune system can help enterprise prevent and eliminate enterprises’ viruses so that enterprises continuously adapt to the environment. This paper uses immunology theory and artificial immune engineering theory, on the basis of previous research, to discuss enterprise immunity, and build evaluation index system of enterprise immunity. Then, we bring out an Evaluation Model of enterprise immunity based on D-S Evidence Theory. FAHP is used to structure and rate the criteria. The information from multiple data sources is combined using Dempstere-Shafer evidence theory. We illustrate our approach by application on the evaluation of Enterprise Immunity.

Research on the Resource Allocation Model for the Satellite Constellation Communication System

Satellite constellation, emerging as a new paradigm for next-generation communicating, enables large-scale application of the geographically and spatially distributed heterogeneous resources for solving problems in science, engineering, and military affairs. The resource allocation in such a large-scale distributed environment is a complex task. Due to the factors that trigger the deployment of resources in satellite constellation communication system, the artificial immune theory is applied to resource allocation field to propose the task-oriented common mathematic model about resource allocation of communication system, which is aimed at the purpose of improving the effectiveness of resource allocation and is based on the 2 important indicators that are communication task’s effectiveness factors and the degree of satisfaction in the communication system. As the immune system has characteristics of self-adaptive, self-learning and self-organization, an immune allocation algorithm that fuzzy processing time is presented by applying the immune theory to resource allocation. Simulation results show that these methods are feasible and efficient in solving the problems of resource allocation for satellite constellation communication system, and the research on this object is a meaningful exploring.

Protection System of Cooperative Network Based on Artificial Immune Theory

The paper proposes a network security monitoring protection model with synergetic mechanism based on the basic principle of human immune system and the state quo of network hacking inspection research and the analysis and appraisal on the original artificial immune model. This model proposes human-computer interaction disaster warning system based on computer dangerous operation and the advantage of agent technology and proposes free-state anti-virus technology based on the human body apoptotic cells. The test results show that the methods proposed in this thesis remedy the illegal program escaped from monitoring security scanning and the system security hidden danger caused manually and at last proposes the development trend of the future artificial immunity in computer protection field.

Psycho-clonal based approach to solve a TOC product mix decision problem

In recent years, theory of constraints (TOC) has emerged as an effective management philosophy that has successfully tackled the problems of profit maximization with known demonstrated bottleneck in traditional as well as modern manufacturing plants. One of the key components of TOC application is to enumerate quantity of the various products to be produced keeping in view the system constraints and this is termed as the TOC product mix decision problem. It is a well-known computationally complex problem and thus warrants the application of heuristics technique or AI based optimization tools to achieve near optimal solutions in real time. To accomplish this objective a new algorithm, Psycho-Clonal has been proposed that works on the principle of artificial immune system and behavioral theory, namely Maslow's need hierarchy theory. Intensive computational experiments have been carried out and superiority of proposed heuristic on a given dataset is established. It is observed that results obtained are better compared to what have been achieved by the TOC heuristic, revised theory of constraint heuristic (RTOC), integer linear programming (ILP), and tabu search based approaches.