Artificial Immune Algorithm Research Articles

Intelligent Monitoring System for Highway Roadbed Based on Combination Neural Network Algorithm

To solve the problem of the frequent occurrence of roadbed faults, we studied the highway roadbed intelligent monitoring system based on a combined neural network algorithm. Based on the embedded system, with a variety of sensors, we completed the construction of the roadbed monitoring system. In the selection of the data processing algorithm model, the combined neural network algorithm based on an artificial immune algorithm and probabilistic neural network (PNN) is selected. The accurate acquisition of data characteristics is realized by data preprocessing, data smoothing and data fitting. Through experimental verification, the accuracy of the research model in identifying roadbed settlements has been improved by about 5% compared to traditional models. Furthermore, the processing time of the model has been shortened by about 19.5%, proving the effectiveness of the model. In terms of fault identification, compared with other classic models, the final recognition accuracy of this model reached 96.7%, far exceeding the comparison model. This provides new ideas for the monitoring and protection of roadbed faults.

Research on single target search and rescue in underwater environment based on machine learning

Machine learning has demonstrated its exceptional performance in addressing complex problems across various domains. This paper proposes an underwater search-rescue system based on the artificial immune algorithm, which effectively solve the problem of finding the optimal local search network in unknown underwater environments and shorten searching time. The system comprises multiple underwater search-rescue robots that form a global search network. Once target victims are identified, the global search network is broken down to form one or more local search networks to track and rescue them. To evaluate the robustness and effectiveness of the system, three indicators were designed: searching time, energy consumption, and tracking distance, and a simulation experiment with a single target is conducted. The results demonstrate that under the identical conditions, the artificial immune algorithm outperforms other methods in underwater target search-rescue tasks.

Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms

Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and automatic identification functions. The distribution and dynamism of cloud databases increase the difficulty of route prediction and recognition in the cloud, affecting the efficiency of cloud computing. In response to the above issues, the author proposes a dynamic path optimization process for cloud databases based on adaptive immune grouping polymorphic ant colony algorithm. By setting up two states of ant colony, reconnaissance ant and search ant, and introducing an adaptive polymorphic ant colony competition strategy, the defect of general ant colony algorithms being prone to falling into local optima is improved; On this basis, an artificial immune algorithm with fast global search capability is further integrated to improve the search ant path optimization process, improving search speed and accuracy. Simulation experiments show that the IPANT algorithm outperforms the other three algorithms, maintaining a throughput of 1000 kbps and relatively stable; The data of OSPF, SPF, and FR are not significantly different, significantly lower than IPANT. The immune polymorphic ant colony algorithm (IPANT) has the lowest time delay for packet routing and performs better than the other three algorithms, with FR and SPF having higher latency. It has been proven that this algorithm can better solve convergence speed and global optimization problems, and can quickly and reasonably find the database to be accessed in the cloud.

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.

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

Experimentative analysis of artificial immune system algorithms for intrusion detection in IoT networks

Experimentative analysis of artificial immune system algorithms for intrusion detection in IoT networks

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.

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.

Hybrid artificial immune algorithm for energy-efficient distributed flexible job shop in semiconductor manufacturing

Hybrid artificial immune algorithm for energy-efficient distributed flexible job shop in semiconductor manufacturing

Leverage Artificial Immune Algorithm to Maintain Data Integrity

Abstract: Robust and efficient data security measures are of the utmost importance given the growing reliance on digital technologies. I recommend developing a simulated security testing platform using artificial immune algorithms to improve data security in response to the needs of the user. This software provides the capacity to simulate different cyber-attacks, making it easier to assess the efficacy of different safeguards. The platform may adapt to these attacks and learn from them by utilizing the strength of artificial immune mechanisms, increasing its resistance to threats in the future. I acquired beneficial expertise in software development, algorithm design, and data security as a result of my active participation in this project. I'm thrilled to share this study as a proof of my abilities and unwavering commitment to the data security area. The suggested approach makes use of Python-based machine learning techniques, and adds HTML, CSS, and JS for the user interface. The suggested approach also includes a user-friendly interface built using HTML, CSS, and JS, making it easier to integrate with present systems.

Integrating Artificial Immune Genetic Algorithm and Metaheuristic Ant Colony Optimizer with Two-Dose Vaccination and Modeling for Residual Fluid Catalytic Cracking Process

Integrating Artificial Immune Genetic Algorithm and Metaheuristic Ant Colony Optimizer with Two-Dose Vaccination and Modeling for Residual Fluid Catalytic Cracking Process

An artificial immune differential evolution algorithm for scheduling a distributed heterogeneous flexible flowshop

This paper studies a distributed heterogeneous flexible flowshop problem (DHFFP) with sequence-dependent setup and transport times, where each job has a release time and each production stage has multiple unrelated parallel machines. An integer linear programming (ILP) model and a new artificial immune differential evolution (AIDE) algorithm are proposed aiming to minimize the makespan and total tardiness simultaneously. Firstly, the AIDE employs a two-dimensional vector encoding scheme including the information of job permutation and factory allocation for solution representation. In addition, a dynamic decoding scheme is designed to construct a feasible schedule. Secondly, three distributed Nawaz–Enscore–Ham (NEH) based constructive heuristics are presented to provide the initial antibody population. Thirdly, a discrete differential evolution algorithm is introduced to mutate the cloned antibodies. Further, clonal suppression is applied by eliminating the antibodies with lower stimulation values. Simulated annealing (SA) based local search is incorporated with four problem-specific neighborhood structures to enhance the algorithm. Finally, the optimal values of the algorithmic parameters are determined by the orthogonal test. Some numerical experiments of the ILP model on small-scale instances are conducted to show the effectiveness of the proposed AIDE algorithm. The AIDE algorithm is also compared with several existing algorithms for different scale instances. The results demonstrate that the AIDE algorithm generates average relative percentage deviation of 12.51% and 7.99% respectively for small & medium-scale instances and large-scale instances, which is the best-performing among all algorithms.

A novel negative selection algorithm with hyper-rectangle detectors based on full coverage of state space for anomaly detection

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.

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.

Network security risk detection method for smart microgrid monitoring system

AbstractAlthough the smart microgrid improves the power quality of the power system, it still has some security risks. So, this paper proposes a network security risk detection method of an intelligent microgrid monitoring system based on the artificial immune algorithm. By analyzing the structure of a smart microgrid monitoring system and introducing Policy Protection Detection Response (P2DR) dynamic network security model and ale static network security analysis method, the network security risk of the microgrid monitoring system is evaluated. The theoretical model is analyzed by the overall framework of the artificial immune algorithm, and its term definition is given. The mutation self is detected by self‐dynamic description and immune cell evolution process with the network structure brought into and the risk of the computing network facing different attack subjects is analyzed. This method has a high detection accuracy for more than 10 kinds of network attack modes, the highest detection accuracy is 98%, and the average time is only 0.8 s. Compared with other methods, this method has higher detection accuracy and faster detection speed. It is universal in various network attack modes and can detect the possible network security risks of the intelligent microgrid monitoring system and protect its safe and stable operation.

Optimization of Rollover Crashworthiness and Vehicle Mass Based on Unreplicated Saturated Factorial Design

In the realm of van design, researchers have been diligently working to enhance rollover crashworthiness while concurrently achieving lightweight body structures. Unlike front and side impacts, rollover crashworthiness is impacted by a greater number of structural dimensions and material parameters. As such, this paper implements an unreplicated saturated factorial design to conduct factor screening for vehicle rollover crashworthiness. This approach effectively and accurately resolves the screening challenges that arise from large numbers of factors, and eliminates dependence on traditional design experience. Consequently, it shortens the design cycle and reduces development costs. In addition, this paper establishes four Kriging approximate models that describe the specific energy absorption and total mass of the key body structure, the displacement of the roof, and the maximum angular velocity of the body’s center of mass. To address the multi-objective optimization problem of improving rollover crashworthiness while reducing mass, this paper combines the particle swarm optimization algorithm with the artificial immune algorithm. This hybrid algorithm converges rapidly, and the Pareto solution set exhibits superior uniformity and diversity. Finally, the shortest distance method is employed to identify the optimal design scheme that can enhance the rollover crashworthiness of vans and reduce the mass of body parts.

A syntactic method for robot imitation learning of complex sequence task

PurposeAiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.Design/methodology/approachThe paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.FindingsIt is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.Practical implicationsUsing this strategy, the robot completes the fourth-order Hanoi tower task has been verified.Originality/valueAn imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.

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.

Hazard prediction of coal and gas outburst based on the Hamming distance artificial intelligence algorithm (HDAIA)

Currently, coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results. Owing to this, an artificial immune algorithm (AIA) was developed for coal and gas outburst prediction based on the Hamming distance (HD) calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm (HDAIA). The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm. The HD algorithm was used to obtain the affinity between the antibody and antigen, and the minimum HD was screened to obtain the prediction result. The collected dynamic data of the drilling cuttings gas desorption index K1 and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province, China, were used as prediction indices. The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts, and it has a good prediction of the risk of coal and gas outbursts. The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.

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.