Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab. Aziz

Recently, there are still many cases of voltage collapse incidents occur all around the world. This is due to the reason that most power systems today are being operated very close to their stability limits because of the exponentially growing demands, the desires to obtain maximum economic benefits and environmental constraints. Therefore, this thesis presents novel techniques for voltage stability evaluation and enhancement in power system. Firstly, a new bus voltage stability index named as Voltage Stability Condition Indicator (VSCI) was developed. The competency of VSCI was corroborated in three tasks; weak bus identification, automatic line outage contingency ranking and weak area identification. In addition, a new method to detect weak areas in a system termed as Weak Area Clustering Margin (WACM) was also developed. In the first part of study, all methods were tested on IEEE 30-bus and IEEE 118-bus test system. Secondly, a new voltage stability prediction technique utilising state of the art machine learning, Support Vector Machine (SVM) was developed. At this stage, two popular SVM selection parameter methods, trial and error and cross validation were investigated and compared. The developed technique used VSCI as the voltage stability indicator to be predicted. The performance of SVM was also compared with the performance of Artificial Neural Network (ANN). To enhance the SVM performance, an outstanding hybrid Artificial Immune Least square Support Vector Machine (AILSVM) that integrates SVM with Artificial Immune System (AIS) was introduced in voltage stability prediction. For comparison, another new hybrid algorithm incorporating ANN and AIS called as Artificial Immune Neural Network (AINN) for voltage stability prediction was also developed. It was found that AILSVM has outclassed AINN significantly in terms of prediction accuracy and computation time. Thirdly, new techniques for load margin improvement were developed. Initially, a superior performance of AIS named as Fast Artificial Immune System (FAIS) to estimate the maximum load margin of a system was developed. FAIS offers a better performance of AIS since several available approaches for cloning, mutation and selection have been explored and compared. The combination of these approaches that delivered the best performance in terms of accuracy and time was utilised in FAIS. Later on, another novel technique that incorporates FAIS and AILSVM known as Fast Artificial Immune Support Vector Machine (FAISVM) for maximum load margin improvement via RPP optimisation was developed. The integration of FAIS and AILSVM has resulted to a very fast and accurate prediction of maximum loading point (MLP) as the objective function for Reactive Power Planning (RPP) optimisation. The proposed technique employed the predetermined support vectors from AILSVM. VSCI was used as the indicator for the MLP of load buses. Another new hybrid algorithm that used Evolutionary Programming (EP) termed as Evolutionary Support Vector Machine (ESVM) was also developed for comparative study. The results showed that FAISVM has outperformed ESVM significantly in terms of load margin improvement, prediction accuracy and computation time. For the second and third part of the study, the developed techniques were tested on IEEE 30- bus test systems. In conclusion, this thesis has developed a new voltage stability index, VSCI and novel techniques known as AILSVM and FAISVM for voltage stability prediction and maximum load margin improvement that utilised biological optimisation method

Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (seemly good) individuals. This type of selection has high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In this study, the Real World Tournament Selection (RWTS) method is incorporated in resource competition phase of AIRS to prevent this issue and experiments are conducted to evaluate the accuracy of new algorithm (RWTSAIRS). The combination of cross validation and t test is used as evaluation method. Algorithms tested on benchmark datasets of the UCI machine learning repository show that RWTSAIRS obtained higher accuracy than AIRS in all cases and that the difference between accuracies of two algorithms was significant in majority of cases

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Application of an AIS to the problem of through life health management of remotely piloted aircraft

The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it

Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network

This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses