Dynamic route guidance algorithm based onartificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.

A Robust Damage Detection Method Developed for Offshore Jacket Platforms Using Modified Artificial Immune System Algorithm

Steel jacket-type platforms are the common kind of the offshore structures and health monitoring is an important issue in their safety assessment. In the present study, a new damage detection method is adopted for this kind of structures and inspected experimentally by use of a laboratory model. The method is investigated for developing the robust damage detection technique which is less sensitive to both measurement and analytical model uncertainties. For this purpose, incorporation of the artificial immune system with weighted attributes （AISWA） method into finite element （FE） model updating is proposed and compared with other methods for exploring its effectiveness in damage identification. Based on mimicking immune recognition, noise simulation and attributes weighting, the method offers important advantages and has high success rates. Therefore, it is proposed as a suitable method for the detection of the failures in the large civil engineering structures with complicated structural geometry, such as the considered case study.

Feature selection for chemical process fault diagnosis by artificial immune systems

With the Industry 4.0 era coming, modern chemical plants will be gradually transformed into smart factories, which sets higher requirements for fault detection and diagnosis(FDD) to enhance operation safety intelligence. In a typical chemical process, there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples, antibody library construction used to be based on experience. It is not only time consuming, but also lack of scientific foundation in fault feature selection, which may deteriorate the FDD performance of the AIS. In this paper, a fault antibody feature selection optimization(FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

Hybrid artificial immune system and extremal optimization algorithm for permutation flowshop scheduling problem

The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.

Smartphone Malware Detection Model Based on Artificial Immune System

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

A Review of Artificial Immune System Based Security Frameworks for MANET

Mobile ad hoc networks (MANETs) are collections of wireless mobile devices that form a communication network with restricted broadcast range, limited resources and without fixed infrastructure. Routing is a critical function in multi-hop MANETs. At the same time, security in MANETs—especially routing security—presents a number of new and interesting challenges. Communication is achieved by relaying data along routes that are dynamically discovered and maintained through collaboration between the nodes. Advances in the field of artificial immune systems provide an opportunity to improve MANET security and performance. Artificial immune systems mimic the functionality of the human immune system wherein there is clear distinction between self and non self and this delineation is important in a MANET where there is no centralized management. The high level of protection provided to the human body by an evolved immune system can be applied as a security feature in MANET. The current security techniques proposed for MANET have varying degrees of success due to the dynamic nature of a MANET. This paper will review different strategies for the application of artificial immune systems to MANETs.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

Intelligent multi-user detection using an artificial immune system

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

Use of self-organizing suppression and q-Gaussian mutation in artificial immune systems

Purpose-The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems(AISs).Design/methodology/approach-The proposed operators are applied in substitution to the suppression and mutation operators used in AISs.The proposed mechanisms were tested in the opt-aiNet,a continuous optimization algorithm inspired in the theories of immunology.The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population.This procedure is computationally expensive,as the Euclidean distances between every possible pair of candidate solutions must be computed.This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality(SOC)phenomenon,which is less dependent on parameter selection.This work also proposes the use of the q-Gaussian mutation,which allows controlling the form of the mutation distribution during the optimization process.The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem:the rigid docking of proteins.Findings-The proposed suppression operator presented some limitations in unimodal functions,but some interesting results were found in some highly multimodal functions.The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark,and also in the docking problem.Originality/value-First,the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet.Second,the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and,as a consequence,a better performance when compared to the traditional Gaussian mutation.

A fuzzy logic resource allocation and memory cell pruning based artificial immune recognition system

In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.

Distance Concentration-Based Artificial Immune Algorithm

The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate.

Improved clustering method based on artificial immune

An improved clustering method based on artificial immune is proposed. To obtain the better initial solution, the initial antibody network is introduced by self organizing map （SOM） method. In the process of the clustering iteration, a series of optimization and evolution strategies are designed, such as clustering satisfaction, the threshold design of scale compression, the learning rate, the clustering monitoring points and the clustering evaluations indexes. These strategies can make the clustering thresholds be quantified and reduce the operator’s subjective factors. Thus, the local optimal and the global optimal clustering simultaneously are proposed by the synthesized function of these strategies. Finally, the experiment and the comparisons demonstrate the proposed method effectiveness.

NEW HYBRID AI-SVM ALGORITHM: COMBINATION OF SUPPORT VECTOR MACHINES AND ARTIFICIAL IMMUNE NETWORKS

Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased.

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

A decision hyper plane heuristic based artificial immune network classification algorithm

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

Fuzzy-Immune PID Control for AMB Systems

In order to improve the dynamic performance of active magnetic hearing systems with highly nonlinear and naturally unstable dynamics, a new nonlinear fuzzy-immune proportional integral-derivative （PID） controller is proposed by combining the immune feedback law with linear PID con trol. This controller consists of a PID controller and a basic immune proportional controller in cascaded connection, the nonlinear function of the immune proportional controller is realized by using fuzzy reasoning. Simulation results demon strate that the active magnetic bearing system with the proposed controller has better dynamic performance and disturbance rejection ability than using the linear PID controller.

Multi-robot hunting strategy based on FIS and artificial immune algorithm

data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule data base and adaptive design considers factors in measuring the hunting efficiency. The optimized rules are applied to the hunting task and the results show that the algorithm can effectively actualize hunting of multiple mobile robots.

Parameter optimization of pharmacokinetics based on artificial immune network

A new method for parameter optimization of pharmacokinetics based on an artificial immune network named PKAIN is proposed. To improve local searching ability of the artificial immune network, a partition-based concurrent simplex mutation is developed. By means of evolution of network cells in the PKAIN artificial immune network, an optimal set of parameters of a given pharmacokinetic model is obtained. The Laplace transform is applied to the pharmacokinetic differential equations of remifentanil and its major metabolite, remifentanil acid. The PKAIN method is used to optimize parameters of the derived compartment models. Experimental results show that the twocompartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.