Artificial Immune System Algorithm Research Articles

An artificial immune system algorithm for classification tasks. An electronic nose case study

This study explores the application of an electronic nose (e-nose), employing an Artificial Immune System (AIS) as a competitive alternative to the typical machine learning techniques. The proposed algorithm's main function is to modify its dimensions and adjust the values of its parameters during the training or mutation process, with no complex operations and minimal parameter tuning. This analytical tool has been applied to the qualitative analysis of chocolate bars, classifying varieties in single and multi-label scenarios. The classification results demonstrate promising results in accurately distinguishing individual chocolate types, obtaining an average accuracy of 97%. Subsequently, multi-label classification (several labels associated with a single chocolate) was executed based on shared ingredient content, achieving an average accuracy of 94%. The evaluation and validation of the proposed AIS were also verified; the obtained performance metrics were compared with those of typical classification techniques, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Satisfactory results show the competitive performance of AIS in single-label classification tasks compared to named techniques. Promisingly, a novel method for multi-label learning in e-noses is obtained to accurately identify different types of chocolates, outperforming methods like KNN and SVM. These results demonstrate the AIS potential as an effective classification method for e-nose applications.

Multi-mode multi-skill resource-constrained project scheduling problem with differentiated professional capabilities

Motivated by a practical situation in a digital transformation project, this paper considers a resource-constrained project scheduling problem with multiple modes, multiple skill types, and differentiated professional capabilities. In the proposed problem, each project activity has one or more alternative execution modes associated with a trade-off between processing time and resource consumption. In an execution mode, an activity requires a certain number of employees with specific skill types and required professional capabilities. A mixed integer programming model is developed to minimize the total project duration. Since this problem is NP-hard, an efficient immunoglobulin-based artificial immune system (EIAIS) algorithm with a new encoding and decoding scheme and novel components is proposed. The effectiveness of the proposed EIAIS algorithm is tested on randomly generated instances. Computational results show that the proposed EIAIS algorithm has better performance than the existing algorithms.

CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.

Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others.

Application of Improved Artificial Immune System Algorithm Based on Applied Mathematics for Optimization of Manpower Allocation in Construction Engineering

The outbreak of the COVID-19 pandemic has led construction companies to prioritize the intelligent and optimal scheduling of human resources in construction projects to reduce costs. This study addresses the problem of heterogeneity in human resource scheduling in construction projects, presents a mathematical model with generic human resources as an example, proposes an improved artificial immune system (NAIS) algorithm to solve the problem, and verifies its effectiveness. Experimental results show that the NAIS algorithm achieves the optimal duration of 9 days in just 2 s using the Matrix Laboratory (MATLAB), which is significantly faster than mathematical optimization technique software (CPLEX), thus confirming the feasibility of the NAIS algorithm. Additionally, the average PD values for the NAIS algorithm, calculated for different worker counts, skills, and the number of tasks, were lower compared to the comparison algorithm. Overall, the NAIS algorithm effectively addresses the heterogeneous problem of human resource scheduling in construction projects with multiple modes, thereby optimizing construction engineering labor allocation.

Energy-saving optimization of air-conditioning water system based on data-driven and improved parallel artificial immune system algorithm

As the air-conditioning water system is designed according to the maximum load, the system will deviate from its optimum state while operating under partial load. Therefore, it is critical that the numerous operating parameters of the various equipments in the system are dynamically adjusted in an effective and timely manner to maximize the operational energy efficiency of the system. To this end, an improved parallel artificial immune system (IPAIS) algorithm is proposed to determine the optimal operating parameters of the system under different loads. Before optimization, the power consumption model is developed using generalized regression neural network (GRNN) combined with mechanism model for each kind of equipment in the system. Afterwards, the optimal control problem is described with the objective of minimizing the total power consumption of all equipments and considering the relevant constraints. Subsequently, the IPAIS is developed to solve the problem by introducing four improvement strategies. Finally, a simulation experiment is conducted using an actual case of an air-conditioning water system. The results show that the developed power consumption model performs well in accuracy, robustness and generalization ability, and the total system energy consumption is reduced by 15.19% after optimization. Meanwhile, the IPAIS is extended to five variants to confirm the functionality and effectiveness of each improved strategy. Furthermore, the optimization performance of IPAIS in the actual system is comprehensively verified and analyzed using an experimental platform. Compared with the comparison algorithms, IPAIS is able to achieve superior optimization results and presents significant advantages in convergence, robustness and computational complexity.

Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

SummaryOne of the major concerns in security and storage improvement of data centers is due to the fact that there is a possibility of access to cloud information, and the distance between data centers for data transfer causes cloud storage problems. In this article, an encryption method using artificial immune system (AIS) is leveraged to secure sensitive data in data centers and particle swarm optimization (PSO) algorithm is exploited to improve the selection of storage services in distributed cloud data centers to store and transfer particular a data set between data centers. To obtain satisfied performance considering the transmitting cost, and smallest transmitting distance, we exploited one discrete PSO algorithm and AIS method and called PSO_AIS. To evaluate the proposed PSO_AIS algorithm, we designed the experiments and simulated it in MATLAB software. In the simulation, we conducted experiments on 13 data centers located in different locations in Iran. The experimental results expose that the proposed algorithm reduces 30% the distance and cost to select a particular data center for a particular data set. Experiments have been carried‐out to demonstrate the effectiveness of the proposed encryption method, which is based on AIS. The results showed that the encoding performance of the proposed method is better than other methods.

Development of a risk assessment methodology for intelligent technology of diagnosing industrial equipment for complex oil and gas facilities

The research is devoted to the development of a risk assessment methodology based on the HAZID (Hazard Identification) method for intelligent diagnostic technology of industrial equipment of oil and gas facilities using the FMEA (Failure Mode and Effects Analysis) approach, modified algorithms of artificial immune systems, a unified artificial immune system, as well as the principles of immunological homeostasis. The results of modeling and experiments are given on the example of diagnostics of the equipment of the industrial facility of the D-304 desorber designed for the regeneration of saturated diethanolamine of the 300/800 unit for gas purification from sulfur compounds (H2S and CO2) of the TengizChevroil oil refinery.

Application of artificial intelligence systems for stylometric analysis of texts as factor of sustainable development

Investment in human capital, along with natural resource management, is an important indicator of sustainable development. One of the areas of such investments is the creation of artificial intelligence systems that allow for the classification of texts. This paper analyzes the use of artificial intelligence systems for stylometric text analysis. On the basis of the algorithm of the convolutional artificial immune system, a system for stylometric analysis of texts was developed and implemented in software. In order to determine the possibility of using this system to determine the authorship of literary works, it was trained and tested. For this, the works of two authors were chosen: Leo Tolstoy and Fyodor Kryukov. This system demonstrated a high quality of text classification and a good speed of work and learning. So, to test the performance of the system, 11 works by Leo Tolstoy and 12 works by Fedor Kryukov were taken that were not used to train the system. All works of these authors were classified correctly. It should be noted that the artificial immune system algorithm can also be successfully used in other tasks requiring text classification.

An artificial immune system algorithm for solving the stochastic multi-manned assembly line balancing problem

An artificial immune system algorithm for solving the stochastic multi-manned assembly line balancing problem

Development of a unified artificial immune system for complex objects control within the framework of the Industry 4.0 concept

The article is devoted to the creation of an intelligent decision-making technology for complex objects control using the bioinspired approach of artificial immune systems (AIS) and the principles of immunological homeostasis for the implementation of the Industry 4.0 program. The bases for creating a unified artificial immune system (UAIS) consisting of modified algorithms of clonal selection, negative selection and immune network have been developed in order to identify the most effective multidimensional dynamic data for processing and predicting. The unification of AIS algorithms based on systematization and classification is used to reduce the variety of modifications and to find the best solution for a specific data set. A technique for evaluating the effectiveness of modified UAIS algorithms based on the principles of immunological homeostasis has been developed. The main stages of the functioning of the proposed technology for complex objects of the oil and gas industry and the advantages of this approach are considered.

Recognition of the emotional state of energy facilities operators based on artificial immune systems

This research paper presents results on the construction of a system for recognizing emotional state of energy facilities operators by the expression of their faces in the images. The model uses one of the algorithms of artificial immune systems as a classifier. An artificial immune system is a concept that contains methods and algorithms that try to apply the basic mechanisms of biological immune system to solve various practical problems. In accordance with the main aim of the research, the article deals with existing concepts of emotion classification as well as current methods for recognizing emotions from facial expressions in images. Also, a brief review of the literature is given in accordance with the questions posed. The proposed model uses a feature extraction approach based on marking the human face with 68 points, followed by extraction of their coordinates. For the obtained coordinates, a new special technique for their processing for subsequent classification is proposed. The built model is able to classify the 7 basic Ekman emotions both on images and in the video stream, with an accuracy of about 80%.

Using Artificial Immune Systems to Sort and Shim Insertion Devices at Diamond Light Source

This work presents the Opt-ID software developed by the Rosalind Franklin Institute (RFI) and Diamond Light Source (DLS) in collaboration with Helmholtz-Zentrum Berlin (HZB). Opt-ID allows for efficient simulation of synchrotron Insertion Devices (ID) and the B fields produced by a given arrangement of candidate magnets. It provides an optimization framework built on the Artificial Immune System (AIS) algorithm for swapping and adjusting magnets within an ID to observe how these changes would affect the magnetic field of a real-world device, guiding ID builders in the steps they should take during ID tuning. Code for Opt-ID is provided open-source under the Apache-2.0 License on Github: https://github.com/rosalindfranklininstitute/Opt-ID

Современные иммунологические модели и их приложения

he paper considers main models and algorithms of artificial immune systems, which are related to the evolutionary computation paradigm and used to search for potential solutions, each of which is represented by an artificial lymphocyte. Same as an individual in evolutionary computation, an artificial lymphocyte is most often encoded by a binary string or a vector of real numbers. As far as the main models of artificial immune systems are concerned, the clonal selection algorithm is close to the evolutionary strategy of evolutionary computing, though it uses more powerful mutation operators and is applied mainly to solve numerical and combinatorial optimisation problems. The negative selection algorithm is based on the "friend or foe" recognition principle found in the immune system and is most popular in applications. The paper presents two aspects of the algorithm: 1) the basic concept, that is, expanding the set of "friend" cells; 2) the goal, which is to learn to distinguish between "friend" and "foe" cells, while only "friend" cell samples are available. We consider continuous and discrete network models representing regulated networks of molecules and cells. We note the advantages and disadvantages of these models and their application in the field of computer security, robotics, fraud and malfunction detection, data mining, text analysis, image recognition, bioinformatics, games, planning, etc.

Application of Artificial Immune Systems in Advanced Manufacturing

In recent years, the application of Advanced Manufacturing Technologies (AMT) in industrial processes represents the introduction of different Advanced Manufacturing Systems (AMS), which encourage enterprises to improve their core competitiveness and maintain sustainable development when facing the increasing demand for personalized product customization. More recently, AMT led to a new Internet revolution, mostly known as 4th Industrial Revolution. Considering the development and deployment of Artificial Intelligence to enable smart and self-behaving industrial systems, autonomic approaches allow the system to adapt itself, eliminating the need for human intervention for management. This paper presents a systematic literature review regarding Artificial Immune Systems (AIS) approaches to tackle multiple AMS problems requiring levels of autonomy. First, a systematic review of current industrial AIS applications in manufacturing environments is presented. Then, a conceptual framework is proposed to bridge the gap between research in the AIS field and the manufacturing industry while discussing key challenges and opportunities to be addressed by future research. This study aims to build a body of knowledge for researchers and manufacturers regarding AIS solutions under Advanced Manufacturing while suggesting directions for understanding the requirements for designing and managing autonomic industry applications supported by AIS. • Presentation of a systematic review of AIS methods application in AMS. • Summary of the empirical evidence of the benefits of applying AIS algorithms in AMS. • Exploration of the readiness level and autonomy of AIS applications in AMS. • Outline the key areas where future research can improve the use of AIS in AMS.

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

Design and optimization of layout problem using meta-heuristic algorithms

Loop layout is a common layout in the Flexible Manufacturing Systems (FMS), machines arranged around the loop and materials are transported in unidirectional only. The intention of the loop layout problem (LLP) is the determination of regulating of machines around a loop, to minimize the maximum congestion among the family of parts. Artificial immune system (AIS) algorithm, Tabu Search (TS) and Improved Tabu Search (ITS) algorithms are employed to solve these loop layout problems. The algorithm is tested and validated through large and small-sized randomly generated hypothetical problems with minimum required machine sequence. Further, the efficiency of algorithms is compared with existing algorithm for benchmark problems. Computational results reveal that ITS algorithm outperforms the AIS, TS and existing algorithm for large-sized hypothetical problems.

Network Intrusion Detection by Artificial Immune System and Neural Network

Easy access, simulation of IOT network increases its application and demands in different area. As many of IOT networks are vulnerable in nature and attracts intruders to take advantage of weak security. This paper has developed a model that can detect the IOT network intrusion. In this work feature optimization was done by use of artificial immune system algorithm. AIS reduces the dimension of the dataset by applying affinity check and cloning steps. Selected features were further use for the traiing of neural network. Trained neural network predict the class of IOT network session (Normal / Malicious). Experiment was done on real dataset of IOT session and result shows that rpopsoed model has improved the detection accuracy as compared o existing models.

Sentiment Analysis on Wearing Mask During COVID-19 Pandemic in India: A Case Study on Twitter

The ongoing pandemic, which was initiated in Wuhan, China, is the rapid spread of coronavirus disease (COVID-19). It has caused public health crises regionally and internationally. According to the World Health Organization (WHO), the virus can spread from an infected person's mouth or nose. Therefore, the WHO has given various guidelines to be followed by people as precautions, among them, wearing a face mask. So, in the proposed paper, sentiment analysis will be performed to study public views on wearing masks, even after getting vaccinated. The purpose of this study is to analyze tweets by Indian citizens on problems due to wearing masks during the COVID-19 pandemic. Data analysis was conducted by several classification algorithms based on Artificial Immune System algorithms (AIS), which are a new deep-learning model for text analysis and performance, and results are also compared with other models such as Naïve Bayes (NB), Support Vector Machines (SVM), etc.

Multiclass anomaly detection for unsupervised and semi-supervised data based on a combination of negative selection and clonal selection algorithms

A key challenge in anomaly detection is the imbalance between the amounts of normal and abnormal signal data. Specifically, the amount of abnormal signal data is considerably less than that of normal signal data. To solve this problem, techniques for detecting abnormalities using only normal signal data derived from artificial immune systems (AISs) have been investigated. A representative example is the negative selection algorithm (NSA), which classifies data and detects anomalies using only normal signals through a process that mimics the underlying principle of vertebrate immunity. However, the NSA is optimized to detect only two classes of anomalies. Therefore, in this study, we developed a multiclass anomaly detection algorithm that hybridizes the principles of NSA and the clonal selection algorithm (CSA). We improved this algorithm using unsupervised and semi-supervised learning algorithms to conveniently detect anomalies at actual industrial sites. This paper presents a process for applying an AIS algorithm to anomaly detection using the evolution of data-based anomaly-detection algorithms. In particular, we leveraged the NSA principle of classification through semi-supervised learning to enable multiple classifications of unlabeled data. The obtained detector data formed clones optimized by the CSA and had constant memory, thus improving the classification accuracy and reducing run time. The proposed algorithm was validated using an intelligent maintenance system bearing dataset and a vacuum deposition equipment dataset.