Artificial Immunity Research Articles

An adaptive detection framework based on artificial immune for IoT intrusion detection system

Given the continual evolution of new network attack methodologies, defenders face the imperative of constantly upgrading security defenses. Current security technologies, albeit effective against known threats, often fall short in handling the intricacies of diverse and novel attacks. Artificial immunity-based network anomaly detection offers a promising avenue by dynamically adapting to evolving threats. However, prevailing algorithms in this domain suffer from low detection rates, limited adaptability, and extended detector generation times. This study aims to tackle these challenges by introducing a high-efficiency network anomaly detection framework, emphasizing both high-dimensional feature selection and adaptive detector generation. Our approach begins with an enhanced dual-module hybrid high-dimensional feature selection method, leveraging evolutionary principles. Furthermore, we introduce a self-sample clustering algorithm based on fuzzy clustering during the tolerance stage, enhancing detector tolerance efficiency. Additionally, an adaptive detector generation scheme is devised. It divides the non-boundary sub-population based on non-self differences and evolution, while employing the red fox optimization algorithm in the boundary region. This adaptive approach dynamically adjusts detector positions and radii to derive optimal detectors. Through comprehensive validation using well-established IoT and network anomaly datasets, our proposed artificial immunity-based IoT intrusion detection framework exhibits superior performance. It achieves higher classification accuracy and lower error rates compared to current state-of-the-art machine learning and artificial immunity algorithms.

PCR-based detection and mutation dynamics of fusion protein gene of orthoaviula viruses sequestered during 2023 field outbreaks in Pakistan

: To isolate and detect a Newcastle disease virus in commercial poultry, Molecular characterization and phylogenetic analysis of the confirmed isolate and Multiple sequence alignment and achievement of accession numbers against our submissions in NCBI bankit.: Genetic and antigenic diversity in the fusion protein gene of New Castle disease virus strains has been recognized and the progressive changes over sequential years indicate that it is a continuously evolving virus. The current vaccines containing conventional vaccinal strains can protect birds to a certain level but do not prevent infection and virus shedding. : The partial fusion protein gene of the 14 NDV isolates during the 2023 outbreaks from different areas of Pakistan was determined and analyzed. The antigenic protein translational segment of the fusion gene nucleotide fragment was targeted with a specifically designed primer executed 202 bp size of predictable amplicon during PCR amplification. The nucleotide sequence analysis of studied isolates showed closed similarity to the NCBI bankit numbers. Phylogenetic analysis revealed that 3 isolates belong to genotype II while, 2 isolates positions near genotype VIII of class II. The 6 isolates were located near genotype XVII and only 1 was presented on genotype V branch in calss II. Mutation analysis results revealed various mutations at nucleotide intervals and even found altered amino acids during translation. The results revealed that nucleotide mutation at various positions attributes amino acid substitution that enables wild prevailing strains to evade artificial active immunity. In such a scenario Chimeric and genotype match vaccines prepared from indigenous isolates may be useful in developing candidate vaccines to prevent virus shedding and infection. Further studies are suggested at molecular level to determine the consensus amino acid sequence for virulent, mesogenic, and avirulent prevailing NDV strains.

Natural versus artificial herd immunity: Is vaccine research investment always optimal?

Under the threat of a rapid expanding virus like the 2020 COVID-19, policy-makers need to decide relatively fast whether and under which conditions to invest in a vaccine, and eventually adopt other protective measures like social distancing or lockdowns, or to wait for natural herd immunity. Taking into account that vaccines take time to be fully developed and effective, this paper considers a unified framework at the crossroad between economics and epidemiology to study optimal public spending in medical research to obtain a vaccine against an infectious disease evolving according to a SIR dynamics. We prove that developed economies always invest in the search of a vaccine. The more individuals care about consumption, the more they actually reduce their current consumption and the more they invest in the vaccine research program to recover their consumption potential at the earliest. Our model would only recommend economies with very poor technology to restrain from investment and wait for herd immunity.

Avian IgY antibodies and its immunotherapeutic applications

Antibodies, also called immunoglobulins, are specialized proteins produced by the immune system in response to the presence of pathogens or foreign substances in the body. These unique proteins are commonly used for diagnostic and therapeutic purposes because they easily bind to antigenic molecules. Polyclonal antibody production currently involves the use of laboratory animals such as rats, rabbits, sheep, goats, and horses. However, the manufacture of these antibodies generally involves practices that cause pain to animals, such as prolonged bloodletting. In recent years, isolating antibodies from egg yolk following hyperimmunization of chickens has emerged as a popular approach for producing significant amounts of antibodies. This approach combines the principles of natural passive immunity and artificial passive immunity. To ensure a continuous accumulation of antibodies in egg yolks, chickens are regularly immunized with specific antigens. Egg yolk antibodies, known as IgY, are extracted and used for immunotherapy and immunodiagnostic purposes in human and animal applications due to their promising antibacterial properties. The antibacterial properties of egg yolk antibodies have been a significant focus in IgY studies. Several reports have shown that IgY helps prevent bacterial transmission or infection in vivo. The production of IgY against mammalian antigens has a higher success rate than IgG production. This is because of the phylogenetic difference between mammals and chickens. Furthermore, these antibodies have a more comprehensive range of antigenic epitope recognition and can respond to more than one species, making them more versatile. This study compiles information on the properties, mechanisms of action, and uses of egg yolk antibodies based on existing literature on IgY technology.

Hamlet and Inoculation

ABSTRACT This article surveys Shakespeare’s biopolitical thought, with a focus on the semantic feature of “inoculat[ion]” in Hamlet. Today, the word “inoculation” means a way to produce artificial immunity by inserting a pathogenic material taken from an external entity through a small incision. This medical procedure, widely practiced in the eighteenth century, is called inoculation because it was like grafting—mixing one body with another. By using “inoculat[ion]” as a rhetorical device, Hamlet explores the politics of immunity before the dawn of modern immunology. Shakespeare’s work negotiates legal and medical thoughts on immunity: in particular, Hamlet is an intriguing text because it balances two different features of immunity, signifying at once juridical exception (as a signature of sovereign authority) and the negative protection of a body (through the metaphor of corpus politicum). Like the metaphor of inoculation (i.e. grafting), a practice that needs the other’s body, Hamlet shows that the body politic can be secured only when the realm elects the same invading foreign entity. In Shakespeare’s early modern imagination, immunity is not a strict boundary between self and non-self; rather, it is a much more negotiable boundary open to alterity and creaturely existences.

The "pharmakon" of prevention: artificial immunity in the covid-19 pandemic.

The concept of pharmakon encapsulates the paradoxical ambivalence of any therapeutic intervention's harmful as well as beneficial effects; within the context of immunological practices related to covid-19, this ambivalence has been successfully exploited by anti-vaccination movements and is also evident in widespread vaccine hesitancy in wealthy countries where vaccines for this virus are widely available. Here we engage with the theoretical apparatus of the pharmakon to examine how care, harm, risk, and prevention are enacted in covid-19 prevention measures and mobilize the transdisciplinary methodologies of science and technology studies to investigate how anticipatory imaginaries drive cutting-edge research on covid-19 vaccines and the clinical and social practices they have elicited.

A synergistic study on farmers’ income enhancement and urban-rural financial development under the perspective of common wealth by an intelligent decision support system for land improvement

Abstract This paper proposes the general framework and technical process of the intelligent decision support system for land improvement based on a multi-objective artificial immunity algorithm. Based on the analysis of the current situation of land use structure and spatial layout in County S, three land resource quantity structure optimization schemes are selected, and the improvement of agricultural land use efficiency and agricultural output value in the three schemes are evaluated and analyzed. The mechanisms that affect income distribution in urban-rural financial development and land finance are summarized. The results show that after land improvement, the area of cultivated land and grassland in County S decreased by 1,444 ha, and the area of garden land and forest land increased to the greatest extent in the economically prioritized scenario. The size of towns increased to 541 ha, and the size of villages decreased to different degrees and all three land improvement programs had the combined effect of increasing the gross agricultural product and boosting farmers’ incomes. The development of urban and rural finance is aided by land improvement, but the degree of inequality in income distribution is widened.

Immunotherapy: Constructive Approach for Breast Cancer Treatment.

A novel and rapid therapeutic approach is the treatment of human breast cancer by enhancing the host's immune system. In initial findings, program death one (PD-1) and program cell death ligand one (PD-L1) showed positive results towards solid tumors, but tumor relapse and drug resistance are the major concerns. Breast cancer therapy has been transformed by the advent of immune checkpoint blockades (ICBs). Triple-negative breast cancers (TNBCs) have exhibited enduring responses to clinical usage of immune checkpoint inhibitors (ICBs) like atezolizumab and pembrolizumab. Nonetheless, a notable proportion of individuals with TNBC do not experience advantages from these treatments, and there is limited comprehension of the resistance mechanisms. Another approach to overcome resistance is cancer stem cells (CSCs), as these cells are crucial for the initiation and growth of tumors in the body. Various cancer vaccines are created using stem cells (dendritic, whole cell, bacterial) and focus primarily on targeting tumor-related antigens. The ultimate objective of cancer vaccines is to immunize the patients by active artificial immunity against cancer, though. In this review, we primarily focused on existing immunotherapeutic options, immune checkpoint blockers, the latest progress in understanding the molecular mechanisms underlying resistance to immune checkpoint inhibitors (ICBs), advanced strategies to overcome resistance to ICBs, cancer stem cell antigens and molecular markers, ongoing clinical trials for BCs and cancer vaccines for breast cancer.

An artificial immunity based intrusion detection system for unknown cyberattacks

The evolving unknown cyberattacks have rapidly expanded the cyber threat landscape. Identifying unknown cyberattacks, therefore, remains a challenging issue, compounded by the widespread implementation of emerging technologies, such as 5G, digital twin, etc. However, most existing intrusion detection systems (IDSs) are effective in detecting only known cyberattacks. In this paper, inspired by artificial immunity (AIm), we propose a hierarchical differential evolution based IDS, coined HiDE-IDS, to identify unknown cyberattacks. Specifically, we first map the multidimensional normal and abnormal network samples to self antigens and nonself antigens, respectively, in a multidimensional geometrical space. Second, a hierarchical differential evolution algorithm for self antigens is designed, to create newly-evolved antigens possibly used for generating cyberattack detectors. Third, a novel filtering mechanism is developed to eliminate invalid new antigens falling into the coverage of either known self or nonself antigens. Last, the remaining new antigens are employed to generate new detectors and further identify known and unknown cyberattacks. Extensive experiments demonstrate that the training efficiency of the proposed HiDE-IDS is significantly improved than recent IDSs. More importantly, while showing a favorable false positive rate of normal data, the HiDE-IDS achieves outperformed effectiveness in recognizing unknown cyberattacks (as well as known cyberattacks) compared to the state-of-the-art studies.

SOSIALISASI DAN PELAYANAN PEMBERIAN VAKSIN COVID-19 DOSIS 1, 2 DAN BOOSTER UNTUK DEWASA DAN LANSIA

The distribution of the Covid-19 vaccine, both primary and booster doses, is intended to provide artificial immunity against the virus that causes Covid-19, reduce the severity of the Covid-19 virus infection and simultaneously create herd immunity. The purpose of implementing this Community Service activity is to assist the role of the Indonesian government in reducing the impact of the Covid-19 pandemic and breaking the chain of transmission of Covid-19 in Indonesia through the provision of Covid-19 vaccine services. The activity was carried out in collaboration with partners from the Kepolisian Sektor Pamulang which provided health workers and vaccines. The object of this vaccination activity is employees and residents around the Swadharma Pondok Cabe Institute of Technology and Business campus environment, namely 104 persons.

Artificial immunity based distributed and fast anomaly detection for Industrial Internet of Things

Recent years have witnessed an increased attack surface of the Industrial Internet of Things (IIoT), as the deep convergence of the Internet of Things (IoT) and other information and communications technologies (ICTs). However, the massive geographically-dispersed IoT devices and the big spatiotemporal data they generated, both pose extreme challenges to traditional centralized and cumbersome anomaly detection systems (ADSs). To meet this gap, in this paper we propose a novel artificial immunity based distributed and fast anomaly detection system, coined AIm-ADS. Specifically, first we develop an artificial immune system based data representation and distributed preprocessing framework, to characterize and prepare the hyper-dimensional IIoT data for anomaly detection (i.e., distinguishing between selfs and nonselfs). Second, we design a new negative selection based hyper-dimensional anomaly detector generation model along with a point-hyperspace (PH) tree based self-detector indexing mechanism, for creating anomaly detectors and indexing self-detectors in a hyperspace. Last, we craft a PH tree based fast anomaly detection method to identify anomalous behaviors of cyberattacks on IIoT. Extensive experiments on three publicly available and widely used real-world datasets (i.e., SDS, SWaT and WADI), demonstrate that the proposed AIm-ADS outperforms existing artificial immunity based and deep learning based anomaly detection studies, in terms of both the detection rate and detection time.

CLASSIFICATION OF BLOOD SUBSTITUTES

Blood substitutes are substances used to replace or supplement the activities of biological blood cellular or acellular components. It is meant to be a transfusion-free option. There are four main categories that blood substitutes fall into: red blood cell substitutes, white blood cell substitutes, platelet substitutes, and plasma derivatives. Red blood cells (RBCs) substitutes can be divided into biological and chemical oxygen carriers. Biological oxygen carriers are haemoglobin-based oxygen carrier (HBOC) and stem cell derived red blood cells (cRBC). Sanguinate is the sole FDA-approved HBOC drug due to its sickle cell reversal, vasodilatory, and noninflammatory qualities while cRBC is utilized to produce universal group of RBCs. It has greater biological connections with natural bloods than chemical oxygen carriers, the second type of RBCs substitutes. When cRBC were transfused into participants, it was found that 63% of the cells continued to circulate in the blood, matching the half-life of a normal RBC, which is 28 days. These showed that red blood cells could be cultivated in a lab and that they also responded well in the human body. A study on stem cell derived red blood cells (cRBC) using growth stimulants, medium cultures, and genetic manipulation to immortalise human erythroid line has yielded mature RBCs. Now, this study is in the clinical trials which portrayed a huge success in the artificial blood field due to its immortal property. Perfluorocarbon (PFC) and polymer-based oxygen carriers are the two subcategories of chemical-based oxygen carriers. Products in this category may not be structurally resemble haemoglobin or RBCs, but they are intended to serve the primary physiological function of blood. Due to the complexity of the cellular parts of the immune system, no alternative to white blood cells (WBC) has been made artificially yet. However, immunotherapy strategies may offer the "functional substitution" for WBC especially in the case of artificial adaptive immunity. There have been approved alternatives for plasma derivatives but none to substitute platelets yet.

Coupled simultaneous analysis of vaccine and self-awareness strategies on evolutionary dilemma aspect with various immunity

On evolutionary game theory (EGT), two intervention policies: vaccination and self-awareness, are considered to account for how human attitude impacts disease spreading. Although these interventions can impose, their implementation may depend on the various immunity systems such as shield immunity, innate immunity, waning immunity, natural immunity, and artificial immunity. This framework provides an epidemic SEIRVA (susceptible-exposed-infected-removed-vaccinated-aware) model and two EGT dynamics to analyze the interplay between the immunity system and social learning interventions. The prospect of exploring the individual's strategy and social dilemma for removing a disease could assist design an effective vaccine program and self-awareness policy. Also, we evaluated the indicator of social efficiency deficit (SED) for a social dual-dilemma to measure the presence of a dilemma situation. Extensive theoretical analysis displays that stability includes the reproduction number, conditions for positivity and uniqueness, and the strength number analyzed in the equilibria, including fundamental properties validated by numerical simulation of the discretization method that appraises a variety of graphs at adjusting parameters. We present extensive numerical studies investigating the affect of controlling parameters, individual vulnerability, optimal policies, and individual costs. It turns out that, even with the affordable vaccine, individuals may have very different behaviors; self-awareness strategy plays a vital role in controlling diseases.

A novel self-learning framework for fault identification of wind turbine drive bearings

The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis–based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity–based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating.

A Secure and Privacy-Preserving E-Government Framework Using Blockchain and Artificial Immunity

Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information.

Vaccines against new coronavirus infection in Russia and the world

The article is devoted to the review of vaccines against new coronavirus infection in the world and Russia.
 Vaccination is an affordable and cost-effective way to reduce morbidity and mortality from COVID-19. The importance of vaccination is beyond doubt and is a method of creating active artificial immunity based on the formed immunological memory to an infectious agent. Conventionally, vaccines can be divided into two groups: classical (recombinant, peptide and virusinactivated), and gene vaccines (vector and mRNA vaccines). Classical vaccines are based on the introduction of ready-made antigens into the body, which can be purified viral proteins, fragments of viral proteins (peptides) or whole inactivated (killed) viral particles. Inactivated whole-virion vaccines contain a weakened or inactivated virus that promotes the development of antiviral immunity. Subunit vaccines contain only surface antigens (specific fragments subunits), which reduces the amount of protein in the vaccine and thereby reduces its allergenicity. Vector vaccines use safe viruses that are unable to reproduce in the human body (vectors), in which a gene is embedded ― a small section of the coronavirus genome. Vector vaccines effectively cause a cellular and humoral immune response, since the vector, entering the cell, is perceived by the body as a viral infection. Gene vaccines differ significantly from classical ones and produce an immune response at the gene level, since they contain not the virus or protein itself, but the genetic material of the SARS-CoV-2 coronavirus. An RNA-based vaccine delivers a specific set of instructions to the bodys cells for the synthesis of a specific protein, to which the bodys immune system must give an immune response.
 The criterion for the effectiveness of the vaccine is the immune response that it creates. The more pronounced and stable it is, the higher the protection against infection. So far, no drugs have been developed that have 100% protection against infection, since the immune response depends on numerous reasons, including individual characteristics of the body, general health, age, etc.
 At the present stage, the tasks of vaccination are complicated by such factors of the epidemiological process as the emergence of new highly pathogenic strains of coronavirus.

Genome-Wide Scan Reveals Toll-Like Receptor Contraction Events in Oplegnathidae.

The striped knifejaw (Oplegnathus fasciatus) and spotted knifejaw (Oplegnathus punctatus) are prominent members of the Oplegnathidae family and are rocky reef-loving fishes with high ecological and economic value. However, the frequent occurrence of diseases in these fishes has severely restricted the development of their breeding industry. Toll-like receptors (TLRs) play an important role in resistance to pathogens as part of innate immunity. Genome-wide scans and cross-species comparative analysis revealed 10 TLRs in O. fasciatus (OfTLRs) and only 5 in O. punctatus (OpTLRs). In contrast to those of mammals and other fishes, the TLR family of Oplegnathidae underwent significant contraction events, especially in O. punctatus (only TLR1, TLR2, TLR14, TLR5, and TLR21 were retained). A phylogenetic tree divided the 10 OfTLRs into 5 subfamilies: TLR1, TLR3, TLR5, TLR7, and TLR11. The five OpTLR genes were divided into three different subfamilies: TLR1, TLR5, and TLR11. Quantitative real-time PCR revealed that all OpTLRs were expressed in the examined tissues, especially the immune system-related tissues, such as the spleen, gill, head kidney, and middle kidney. The expression of OpTLRs was high at the early stage of development (5 days posthatching [dph]) and decreased gradually until 30 dph. We speculated that maternal immunity or the developmental function of TLRs played an important protective role in the early stage. However, from 30 to 60 dph, TLR expression was low. At this time, juvenile fish are susceptible to viruses and begin to show TLR self-expression with weak immunity. Artificial immunity enhancement is needed to improve the environmental resistance of juvenile fish. In summary, our results not only provide valuable basic data for future studies of the TLR gene family in Oplegnathidae fish but also lay a solid foundation for Oplegnathidae fish research.

Dynamic analysis of mean-field and fractional-order epidemic vaccination strategies by evolutionary game approach

Individual's perception of the participant in a vaccine program reflects their intrinsic appreciation of the trade-off between vaccine behavior, risk of infection, and memory effect. Here we present a mean-field approximation and fractional-order model embedded with the evolutionary game theory (EGT) process for susceptible-vaccinated-infected-recovered (SVIR) epidemic dynamics, where the two types of immunity, artificial and natural immunity, are considered. Besides the well-known vaccination game models, we successfully establish a theoretical approach of fractional-order dynamics for vaccination games in which epidemic spread and individual decisions are supposed to evaluate social behavior. The EGT governs the strategy adoption, while the fractional-order process directs the memory effect state. Our analytical forecasts are validated by numerical simulation of the finite difference method and Adams-Bashforth-Moulton algorithm for the mean-field and Caputo fractional-order derivative that assesses various graphs at varying parameters. Our results show that an effective vaccine may meaningfully minimize the risk of infection. However, despite vaccines' obvious impacts and advantages on a community, many individuals choose not to vaccinate for various reasons, leading to anti-vaccination groups and vaccine apprehension. In this aspect, people are interested in gaining natural immunity. Notably, the individual's risk perception is fundamental for controlling the infection, while the fractional-order dynamics mainly dictate the degree of freedom with memory effect. Higher fractional order with EGT leads to an improved vaccination intake, while a cheaper and reliable vaccine ensures to lessening of contagious disease. The proposed model captures relevant disease behavior and the memory effect of a synchronized pandemic event, emphasizing the underlying role of social schemes.

Construction of Mobile Screening Movie Recommendation Model Based on Artificial Immune Algorithm

The mobile screening of digital movies can fully take into account the viewing experience of scattered areas. As a public cultural service system, it is playing a pivotal role. The consistency of the film screened with the tastes of the audience in the service area of the screening team has largely affected the quality of rural public culture services. Traditional recommendation algorithms directly use raw data to make predictions, leading to deviations in predictions. This article draws on the principles of immune recognition, clone selection, immune mutation, and self-adaptation of the artificial immune system to improve the recommendation effect of single-type data, the recommendation effect of sparse data, and the recommendation effect of project cold start problems and discusses the recommendation based on artificial immunity. For the single type of data, there are only positive samples, which leads to the problem that the training results are all positive. This paper proposes a single-class recommendation algorithm based on artificial immunity. The algorithm uses the positive and negative sample addition method proposed in this paper to add positive and negative samples related to user selection, so as to effectively solve the problem of difficult definition of data negative samples. Then, the artificial immune network is used to cluster the users of various activities, reduce the size of the candidate neighbor set, calculate the user’s nearest neighbor set, and give recommendations.

The RapidIO Routing Strategy Based on the Double-Antibody Group Multi-Objective Artificial Immunity Algorithm.

The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.