Showing papers on "Artificial immune system published in 2015"

Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration

Abstract: In order to optimize their assets, electrical power distribution companies seek out various techniques to improve system operation and its different variables, like voltage levels, active power losses and so on. A few of the tools applied to meet these objectives include reactive power compensation, use of voltage regulators, and network reconfiguration. One target most companies aim at is power loss minimization; one available tool to do this is distribution system reconfiguration. To reconfigure a network in radial power distribution systems means to alter the topology changing the state of a set of switches normally closed (NC) and normally opened (NO). In restructured electrical power business, a company must also consider obtaining a topology as reliable as possible. In most cases, reducing the power losses is no guarantee of improved reliability. This paper presents a multiobjective algorithm to reduce power losses while improving the reliability index using the artificial immune systems technique applying graph theory considerations to improve computational performance and Pareto dominance rules. The proposed algorithm is tested on a sample system, 14-bus test system, and on Administracion Nacional de Electricidad (ANDE) real feeder (CBO-01 23-kV feeder).

A survey on context-aware recommender systems based on computational intelligence techniques

Abstract: The demand for ubiquitous information processing over the Web has called for the development of context-aware recommender systems capable of dealing with the problems of information overload and information filtering. Contemporary recommender systems harness context-awareness with the personalization to offer the most accurate recommendations about different products, services, and resources. However, such systems come across the issues, such as sparsity, cold start, and scalability that lead to imprecise recommendations. Computational Intelligence (CI) techniques not only improve recommendation accuracy but also substantially mitigate the aforementioned issues. Large numbers of context-aware recommender systems are based on the CI techniques, such as: (a) fuzzy sets, (b) artificial neural networks, (c) evolutionary computing, (d) swarm intelligence, and (e) artificial immune systems. This survey aims to encompass the state-of-the-art context-aware recommender systems based on the CI techniques. Taxonomy of the CI techniques is presented and challenges particular to the context-aware recommender systems are also discussed. Moreover, the ability of each of the CI techniques to deal with the aforesaid challenges is also highlighted. Furthermore, the strengths and weaknesses of each of the CI techniques used in context-aware recommender systems are discussed and a comparison of the techniques is also presented.

Self-adaptive attribute weighting for Naive Bayes classification

Abstract: Self-adaptive attribute weighting for Naive Bayes classification.Using Artificial Immune Systems (AIS) for attribute weighting.Seamlessly integrating learning objective and AIS affinity function for attribute weighting.Experiments on 42 real-world datasets demonstrating superb performance gain. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance.

A cascade reaction network mimicking the basic functional steps of adaptive immune response

Abstract: Biological systems use complex 'information-processing cores' composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

A lifelong learning hyper-heuristic method for bin packing

Abstract: We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems

Abstract: Artificial immune system algorithm (AIS) is a population-based global heuristic optimisation algorithm. It is inspired by immune system of human bodies. Alleviating premature convergence problem of heuristic optimisation algorithms is a hot research area. In this study, chaotic-based strategies are embedded into AIS to alleviate its premature convergence problem. Four various chaotic-based AIS strategies with five different chaotic map functions (totally 20 cases) are examined, and the best one is chosen as the best chaotic paradigm for AIS. The results of applying the proposed chaotic AIS to a variety of unimodal and multimodal benchmark functions reveal that it offers high-quality solutions. It significantly outperforms conventional AIS and gravitational search algorithm. The outperformance is both in terms of accuracy of solutions and stability in finding accurate solutions.

To err is robotic, to tolerate immunological: fault detection in multirobot systems.

Abstract: Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.

Minimizing makespan for flow shop scheduling problem with intermediate buffers by using hybrid approach of artificial immune system

Abstract: Permutation flow shop scheduling problem with intermediate buffer is presented.Minimization of Makespan is considered as objective functions.Three new meta-heuristics are presented.Hybrid artificial immune system and artificial immune system are proposed. In this study, three new meta-heuristic algorithms artificial immune system (AIS), iterated greedy algorithm (IG) and a hybrid approach of artificial immune system (AIS-IG) are proposed to minimize maximum completion time (makespan) for the permutation flow shop scheduling problem with the limited buffers between consecutive machines. As known, this category of scheduling problem has wide application in the manufacturing and has attracted much attention in academic fields. Different from basic artificial immune systems, the proposed AIS-IG algorithm is combined with destruction and construction phases of iterated greedy algorithm to improve the local search ability. The performances of these three approaches were evaluated over Taillard, Carlier and Reeves benchmark problems. It is shown that the AIS-IG and AIS algorithms not only generate better solutions than all of the well-known meta heuristic approaches but also can maintain their quality for large scale problems.

A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring

Abstract: Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.

A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement

Abstract: Image enhancement means to improve the perception of information in images. Histogram equalization (HE) and linear contrast stretching (LCS) are the commonly used methods for image enhancement. But images obtained through these processes, generally, have excessive contrast enhancement due to which they are not suitable for use in fields where brightness is of critical importance. In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) along with Negative Selection Algorithm, a model of artificial immune system, is proposed for image enhancement which is achieved by enhancing the intensity of the gray levels of the images. The proposed algorithm is applied to histogram equalized images of lathe tool and MATLAB inbuilt images to verify its effectiveness. The results are compared with conventional enhancement techniques such as HE, LCS and Standard PSO algorithm based image enhancement.

Immuno-inspired robotic applications

Abstract: Graphical abstractDisplay Omitted HighlightsImmunity-based robotic applications are reviewed according to immunological models; old and newMathematical details of reported literature are tabulated genealogicallyIssues pertaining to validity of immunological models are raisedWe have suggested immunological equivalents of various support functions in these applicationsModern trends in robotics are emphasized in conjunction with those in immunology The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications Generally, immunity-based robotic applications map local instantaneous sensory information into either an antigen or a co-stimulatory signal, according to the choice of representation schema Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells, in terms of actuation signals It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead, resulting in an ad-hoc manner these applications are reported On the other hand, the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications Authors, therefore, present a detailed review of immuno-inspired robotic applications in an attempt to identify the possible areas to explore Moreover, the literature has been categorized according to the underlying immuno-definitions Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary, real or hybrid approaches Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory As a result of this study, authors suggest a renewed focus on innate immunity, action contextualization prior to B/T cell invocation and behavior evolution instead of arbitration In this context, a multi-tier immunological framework for robotics research, combining innate and adaptive components together is also suggested and skeletonized

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Abstract: A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

Dynamic local search based immune automatic clustering algorithm and its applications

Abstract: Ring topology of neighborhood in Mutation Strategy 2. Besides four different local search operation includes external cluster swapping, internal cluster swapping, cluster addition and cluster decrease is proposed to realize variation of the number of clusters during evolution, a neighborhood structure based hybrid mutation strategy provides a proper tradeoff between exploration and exploitation. Dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partitioning of data sets.A dynamic local search is designed to find the optimal number of clusters with a fast speed.The dynamic local search includes external cluster swapping, internal cluster swapping, cluster addition and cluster decrease.A new neighborhood structure based hybrid mutation strategy is proposed to further improve the performance of the algorithm.DLSIAC is also applied to several texture images and SAR images segmentation, with a good performance obtained. Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets. The real based antibody encoding consists of the activation thresholds and the clustering centers. Then based on the special structures of chromosomes, a particular dynamic local search scheme is proposed to exploit the neighborhood of each antibody as much as possible so to realize automatic variation of the antibody length during evolution. The dynamic local search scheme includes four basic operations, namely, the external cluster swapping, the internal cluster swapping, the cluster addition and the cluster decrease. Moreover, a neighborhood structure based clonal mutation is adopted to further improve the performance of the algorithm. The proposed algorithm has been extensively compared with five state-of-the-art automatic clustering techniques over a suit of datasets. Experimental results indicate that the DLSIAC is superior to other five clustering algorithms on the optimum number of clusters found and the clustering accuracy. In addition, DLSIAC is applied to a real problem, namely image segmentation, with a good performance.

Parameter extraction of solar PV double diode model using artificial immune system

Abstract: In this paper, a meta-heuristics algorithm based artificial immune system (AIS) is used for solar PV parameter estimation. A new objective function based on derivative of maximum power with respect to voltage for solar double diode model is proposed. For performance evaluation and validation of the proposed approach using AIS, the results are compared with Genetic algorithm (GA) and particle swarm optimization (PSO) for two different PV modules. The results prove that the proposed approach with AIS outperforms GA and PSO in terms of convergence speed and objective function value for both PV modules. The proposed formulation with AIS can be used for parameter extraction of panel with different make/models.

A Novel Clonal Selection Algorithm for Community Detection in Complex Networks

Abstract: Recent years have seen the arising recognition of community detection in complex networks. Artificial immune systems, owing to their inherent properties, have been thoroughly studied and well applied to practical use. In this article, one of the well-known artificial immune system models, named clonal selection algorithm, is introduced to reveal community structures in complex networks. By introducing a novel antibody population initialization mechanism and a novel hypermutation strategy, the proposed approach could be applied to moderate-scale network. Besides, by optimizing an objective function called modularity density, the proposed algorithm is also capable of detecting community structure at multiple resolution levels. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

Anomaly detection in Wireless Sensor Networks using immune-based bioinspired mechanism

Abstract: During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.

An immune optimization based real-valued negative selection algorithm

Abstract: Negative selection algorithms are important for artificial immune systems to produce detectors. But there are problems such as high time complexity, large number of detectors, a lot of redundant coverage between detectors in traditional negative selection algorithms, resulting in low efficiency for detectors' generation and limitations in the application of immune algorithms. Based on the distribution of self set in morphological space, the algorithm proposed in this paper introduces the immune optimization mechanism, and produces candidate detectors hierarchically from far to near, with selves as the center. First, the self set is regarded as the evolution population. After immune optimization operations, detectors of the first level are generated which locate far away from the self space and cover larger non-self space, achieving that fewer detectors cover as much non-self space as possible. Then, repeat the process to obtain the second level detectors which locate close to detectors of the first level and near the self space and cover smaller non-self space, reducing detection loopholes. By analogy, qualified detector set will be obtained finally. In detectors' generation process, the random production range of detectors is limited, and the self-reaction rate between candidate detectors is smaller, which effectively reduces the number of mature detectors and redundant coverage. Theoretical analysis demonstrates that the time complexity is linear with the size of self set, which greatly reduces the influence of growth of self scales over the time complexity. Experimental results show that IO-RNSA has better time efficiency and generation quality than classical negative selection algorithms, and improves detection rate and decreases false alarm rate.

Wavelet-artificial immune system algorithm applied to voltage disturbance diagnosis in electrical distribution systems

Abstract: This study presents a new approach to detecting and classifying voltage disturbances in electrical distribution systems based on wavelet transform and artificial immune algorithm. This proposal unifies the negative selection artificial immune algorithm with the discrete wavelet transform concept. Thus, the measurements obtained in a distribution substation by the supervisory control and data acquisition acquisition system are transformed into the wavelet domain. Afterward, a negative selection artificial immune system realises the diagnosis, identifying and classifying the abnormalities. The principal application of this tool is to aid the system operation during faults as well as to supervise the protection system. To evaluate the performance of the proposed method, two distribution systems were modelled in EMTP software: an 84-bus test system and a 134-bus real system. The results show a good performance, emphasising the precision of the diagnosis.

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

Abstract: The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

Cost optimization of water distribution networks by using artificial immune systems

Abstract: This study aims at the development of an optimization model based on artificial immune systems (AIS) to minimize cost designs of water distribution networks (WDNs). Clonal selection algorithm (Clonalg), a class of AIS, was used as an optimization technique in the model, and its mutation operation was modified to increase the diversity (search capability). EPANET, a widely known WDN simulator, was used in conjunction with the proposed model. The model was applied to four WDNs of Two-loop, Hanoi, Go Yang, New York City, and the results obtained were compared with other heuristic and mathematical optimization models in the related literature, such as harmony search, genetic algorithm, immune algorithm, shuffled complex evolution, differential evolution, and non-linear programming-Lagrangian algorithm. Furthermore, the modified Clonalg was compared with the classic Clonalg in order to demonstrate the impact of the modification on the diversity. The proposed model appeared to be promising in terms of cost designs of WDNs.

Fault diagnosis using noise modeling and a new artificial immune system based algorithm

Abstract: A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

Adaptive classification model based on artificial immune system for breast cancer detection

Abstract: Early stage asymmetric signs in breast that can be captured by the screening-digital mammography can be used for a precocious diagnosis of breast cancer. Conventional mammography screening fails to detect subtle anomalies, so computer-aided methods are studied in order to improve the accuracy of image analysis. To classify the images into asymmetric and normal cases, in this paper we investigated the performance of an Adaptive Artificial Immune System (A2INET) classifier. To test the efficiency of the algorithm, two public datasets have been considered: 32 pairs of mammographic images including MLO projection retrieved from Digital Database for Screening mammographic (DDSM) and 30 ones from Mammographic Image Analysis Society (mini-MIAS) databases. Results show that A2INET yields best results with respect to the other more conventional classifiers.

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

Abstract: Graphical abstractWe present two artificial immune models in mutitemporal synthetic aperture radar images change detection. The first model, as shown in Fig. (a), simulates the immune process to classify pixels in the difference image into two classes, changed class and unchanged class. The ag is one of the pixel and its affinity with antibodies and memory cells contains local information to reduce the impact of speckle noise. The second model, shown in Fig. (b), executes immune process in a fuzzy way to improve the accuracy of change detection results. Display Omitted HighlightsWe propose a novel change detection approach for synthetic aperture radar images based on unsupervised artificial immune systems.To reduce the impact of speckle noise, we incorporate local information into antibody-antigen affinity.We perform the immune response process in a fuzzy way to get an accurate result by retaining more image details.The proposed model performs well on several kinds of difference images and can be applied to the tasks of change detection. In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.