scispace - formally typeset
Search or ask a question

Showing papers on "Artificial immune system published in 2012"


Journal ArticleDOI
TL;DR: This paper presents a novel supervised AIN, the artificial antibody network (ABNet), based on immune network theory-aimed at performing multi-/hyperspectral remote sensing image classification, and demonstrates that ABNet has remarkable recognizing accuracy and ability to provide effective classified imagery, superior to other methods.
Abstract: The artificial immune network (AIN), a computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to multi-/hyperspectral remote sensing image classification has been severely restricted. This paper presents a novel supervised AIN-namely, the artificial antibody network (ABNet), based on immune network theory-aimed at performing multi-/hyperspectral image classification. To construct the ABNet, the artificial antibody population (AB) model was utilized. AB is the set of antibodies where each antibody has two attributes-its center vector and recognizing radius-thus each can recognize all antigens within its recognizing radius. In contrast to the traditional AIN model, ABNet can adaptively obtain these two parameters by evolving the antigens without relying on user-defined parameters in the training step. During the process of training, to enlarge the recognizing range, the immune operators (such as clone, mutation, and selection) were used to enhance the AB model to find better antibody in the feature space, which may recognize as much antigen as possible. After the training process, the trained ABNet was utilized to classify the remote sensing image, exhibiting superior learning abilities. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm in comparison to other supervised classification algorithms: minimum distance, Gaussian maximum likelihood, back-propagation neural network, and our previously developed artificial immune classifiers-resource-limited classification of remote sensing image and multiple-valued immune network classifier. The experimental results demonstrate that ABNet has remarkable recognizing accuracy and ability to provide effective classification for multi-/hyperspectral remote sensing imagery, superior to other methods.

186 citations


Journal ArticleDOI
01 Aug 2012
TL;DR: An immunity enhanced particle swarm optimization (IEPSO) algorithm, which combines particle swarm optimizations with the artificial immune system, is proposed for damage detection of structures and results show that the proposed strategy is efficient on determining the sites and the extents of structure damages.
Abstract: An immunity enhanced particle swarm optimization (IEPSO) algorithm, which combines particle swarm optimization (PSO) with the artificial immune system, is proposed for damage detection of structures. Some immune mechanisms, selection, receptor editing and vaccination are introduced into the basic PSO to improve its performance. The objective function for damage detection is based on vibration data, such as natural frequencies and mode shapes. The feasibility and efficiency of IEPSO are compared with the basic PSO, a differential evolution algorithm and a real-coded genetic algorithm on two examples. Results show that the proposed strategy is efficient on determining the sites and the extents of structure damages.

160 citations


Journal ArticleDOI
TL;DR: Improvements in the simple clonal selection strategy are improved and a novel immune clonal algorithm (NICA) is proposed, which in most problems is able to achieve much better spread of solutions and better convergence near the true Pareto-optimal front.
Abstract: Research on multiobjective optimization (MO) becomes one of the hot points of intelligent computation. Compared with evolutionary algorithm, the artificial immune system used for solving MO problems (MOPs) has shown many good performances in improving the convergence speed and maintaining the diversity of the antibody population. However, the simple clonal selection computation has some difficulties in handling some more complex MOPs. In this paper, the simple clonal selection strategy is improved and a novel immune clonal algorithm (NICA) is proposed. The improvements in NICA are mainly focus on four aspects. 1) Antibodies in the antibody population are divided into dominated ones and nondominated ones, which is suitable for the characteristic of one multiobjective optimization problem has a series Pareto-optimal solutions. 2) The entire cloning is adopted instead of different antibodies having different clonal rate. 3) The clonal selection is based on the Pareto-dominance and one antibody is selected or not depending on whether it is a nondominated one, which is different from the traditional clonal selection manner. 4) The antibody population updating operation after the clonal selection is adopted, which makes antibody population under a certain size and guarantees the convergence of the algorithm. The influences of the main parameters are analyzed empirically. Compared with the existed algorithms, simulation results on MOPs and constrained MOPs show that NICA in most problems is able to And much better spread of solutions and better convergence near the true Pareto-optimal front.

133 citations


Journal ArticleDOI
Mousumi Basu1
TL;DR: In this article, the authors presented an artificial immune system algorithm for solving the combined heat and power economic dispatch problem, which is based on the clonal selection principle which implements adaptive cloning, hypermutation, aging operator and tournament selection.

90 citations


Journal ArticleDOI
TL;DR: The use of an Artificial Immune System (AIS), which emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks, on one aspect of security management, viz. the detection of credit card fraud.
Abstract: Some biological phenomena offer clues to solving real-life, complex problems. Researchers have been studying techniques such as neural networks and genetic algorithms for computational intelligence and their applications to such complex problems. The problem of security management is one of the major concerns in the development of eBusiness services and networks. Recent incidents have shown that the perpetrators of cybercrimes are using increasingly sophisticated methods. Hence, it is necessary to investigate non-traditional mechanisms, such as biological techniques, to manage the security of evolving eBusiness networks and services. Towards this end, this paper investigates the use of an Artificial Immune System (AIS). The AIS emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks. The paper discusses the use of AIS on one aspect of security management, viz. the detection of credit card fraud. The solution is illustrated with a case study on the management of frauds in credit card transactions, although this technique may be used in a range of security management applications in eBusiness.

70 citations


Journal ArticleDOI
TL;DR: An agent-based IDS inspired by the danger theory of human immune system is proposed, where multiple agents are embedded to ABIDS, where agents coordinate one another to calculate mature context antigen value (MCAV) and update activation threshold for security responses.

68 citations


Journal ArticleDOI
TL;DR: A new multiclass classifier based on immune system principles is proposed, which has the embedded property of local feature selection, and shows good performance in comparison with both other immune inspired classifiers and other classifiers in general.
Abstract: A new multiclass classifier based on immune system principles is proposed. The unique feature of this classifier is the embedded property of local feature selection. This method of feature selection was inspired by the binding of an antibody to an antigen, which occurs between amino acid residues forming an epitope and a paratope. Only certain selected residues (so-called energetic residues) take part in the binding. Antibody receptors are formed during the clonal selection process. Antibodies binding (recognizing) with most antigens (instances) create an immune memory set. This set can be reduced during an optional apoptosis process. Local feature selection and apoptosis result in data-reduction capabilities. The amount of data required for classification was reduced by up to 99%. The classifier has only two user-settable parameters controlling the global-local properties of the feature space searching. The performance of the classifier was tested on several benchmark problems. The comparative tests were performed using k-NN, support vector machines, and random forest classifiers. The obtained results indicate good performance of the proposed classifier in comparison with both other immune inspired classifiers and other classifiers in general.

59 citations


Journal ArticleDOI
TL;DR: In this article, the authors developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), which forms the class of unsupervised classification or automatic facies identification followed by facies map generation.
Abstract: Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.

51 citations


Journal ArticleDOI
TL;DR: An immune-inspired system is described based on an alternate theory about the self-nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells.
Abstract: This paper describes an immune-inspired system based on an alternate theory about the self-nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even define new data monitoring in order to detect an extreme variation of the system behavior, which means anomalies occurrences. Through these algorithms, tests are performed to detect faults of a DC motor. Upon detection of faults, a participatory clustering algorithm is used to classify these faults and tested to obtain the best set of parameters to achieve the most accurate clustering for these tests in the application being discussed in the article.

42 citations


Proceedings ArticleDOI
29 May 2012
TL;DR: A dynamical risk assessment method for IoT inspired by Artificial Immune System is proposed in this paper, made up of Detection Agent of Attack and Sub-system of Dynamical Risk Assessment.
Abstract: The Internet of Things (IoT) confronts a complicated and changeful attack environment It is necessary to evaluate the security risk of IoT dynamically to judge the situation of IoT To resolve the above problem, a dynamical risk assessment method for IoT inspired by Artificial Immune System is proposed in this paper The proposed method is made up of Detection Agent of Attack and Sub-system of Dynamical Risk Assessment Furthermore, it adopts the technology of detector distribution The simulation of immune principles and mechanisms in the real IoT environment is deduced by set theory in math The attack detector evolves dynamically in the IoT immune environment Its change forms the dynamical security risk value of IoT

36 citations


Journal ArticleDOI
TL;DR: The experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.
Abstract: Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation The algorithm consists of three steps First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board Then feature patterns are obtained to identify the fault using current signals Second, the fault related features are extracted from three-phase currents Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults The proposed method was experimentally implemented on a 037 kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors

Journal ArticleDOI
TL;DR: In this article, the authors proposed an integrated model of dynamic cellular manufacturing and supply chain design with consideration of various issues such as multi-plant locations, multiple markets, multi-time periods, reconfiguration, etc.
Abstract: In a global dynamic environment, there is a need to develop organizations and facilities significantly more flexible and responsive. This work proposes an integrated model of dynamic cellular manufacturing and supply chain design with consideration of various issues such as multi-plant locations, multiple markets, multi-time periods, reconfiguration, etc. The model objective was to minimize the sum of various costs such as facility/plant to market transportation cost, part holding cost at a facility/plant, part outsourcing cost, machine procurement cost, machine maintenance overhead cost, machine repair cost, production loss cost due to machine breakdown, machine operation cost, setup cost, tool consumption cost, inter-cell travel cost, intra-cell travel cost, and system reconfiguration cost for the entire planning time horizon. To study the model, three procedures—LINGO, artificial immune system, and hybrid artificial immune system—are used to perform computational experiment on some problems from existing literature. The best result generally is found by the hybrid artificial immune system algorithm.

Journal ArticleDOI
TL;DR: Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times and artificial immune systems which mimic the behavior of the natural immune system for solving complex optimization problems.
Abstract: Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image.

Journal ArticleDOI
TL;DR: Artificial Immune System method effectively tackles Dial-a-ride problem by providing us with optimal solutions, and is concluded to be the most effective method.
Abstract: Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions.

Proceedings ArticleDOI
02 May 2012
TL;DR: In this model, which is based on Artificial Immune Recognition System, user behavior is considered, the model puts together the two methodologies of fraud detection, namely tracking account behavior and general thresholding.
Abstract: In this paper we present a new model based on Artificial Immune System for credit card fraud detection. In this model, which is based on Artificial Immune Recognition System, user behavior is considered. The model puts together the two methodologies of fraud detection, namely tracking account behavior and general thresholding. The system generates normal memory cells using each user's transaction records, yet fraud memory cells are generated based on all fraudulent records. To get more accurate results, we have performed analysis on training data in order to control the number of memory cells. During the test phase each user's transaction is presented to his/her own normal memory cells, together with fraud memory cells.

Journal ArticleDOI
TL;DR: Results from the hardware-in-the-loop studies show that the AIS controllers can provide effective control of all generators' terminal voltages during pulsed loads, restoring and stabilizing them quickly.
Abstract: An artificial immune system (AIS)-based control of generator excitation systems for the U.S. Navy's electric ship is presented in this paper to solve power quality problems caused by high-energy loads such as direct energy weapons. The coordinated development of the AIS controllers mainly consists of two parts-innate immunity (optimal) and adaptive immunity. The parameters of the controllers for the former, to provide optimal performance, are determined simultaneously using particle swarm optimization. For dramatic changes in the ship's power system, adaptive control based on the immune system feedback law is developed. The feedback law adapts the controllers' parameters only during transient disturbances. After the disturbance, the controllers' parameters are restored to their innate values. A ship's real-time power system and the proposed AIS control of all excitation systems have been implemented on a real-time digital simulator and a digital signal processor, respectively. Results from the hardware-in-the-loop studies show that the AIS controllers can provide effective control of all generators' terminal voltages during pulsed loads, restoring and stabilizing them quickly.

Journal ArticleDOI
TL;DR: An attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network using a relatively newly discovered optimization algorithm entitled, artificial immune system.
Abstract: The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.

Journal ArticleDOI
TL;DR: The study indicates that the proposed algorithm yields a much better spread of solutions and converges closer to the true Pareto frontier compared with The Non-dominated Sorting Genetic Algorithm and Improving the Strength Pare to Evolutionary Algorithm.
Abstract: Macro-evolution is a new kind of high-level species evolution inspired by the dynamics of species extinction and diversification at large time scales. Immune algorithms are a set of computational systems inspired by the defense process of the biological immune system. By taking advantage of the macro-evolutionary algorithm and immune learning of artificial immune systems, this article proposes a macro-evolutionary multi-objective immune algorithm (MEMOIA) for optimizing multi-objective allocation of water resources in river basins. A benchmark test problem, namely the Viennet problem, is utilized to evaluate the performance of the proposed new algorithm. The study indicates that the proposed algorithm yields a much better spread of solutions and converges closer to the true Pareto frontier compared with The Non-dominated Sorting Genetic Algorithm and Improving the Strength Pareto Evolutionary Algorithm. MEMOIA is applied to a water allocation problem in the Dongjiang River basin in southern China, with three objectives named economic interests (OF1), water shortages (OF2) and the amount of organic pollutants in water (OF3). The results demonstrate the capabilities of MEMOIA as well as its suitability as a viable alternative for enhanced water allocation and management in a river basin.


Proceedings ArticleDOI
10 Jun 2012
TL;DR: A novel framework for automatic feature selection in brain-computer interfaces (BCIs), which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is presented using a state-of-the-art artificial immune network, the cob-aiNet.
Abstract: In this work, we present a novel framework for automatic feature selection in brain-computer interfaces (BCIs). The proposal, which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is based on feature optimization (with both binary and real coding) using a state-of-the-art artificial immune network, the cob-aiNet. In order to analyze the performance of the proposed framework, two approaches are adopted: a direct use of the Davies-Bouldin index and the use of metrics associated with the operation of an extreme learning machine (ELM) in the role of a classifier. The results reveal that the proposal has the potential of improving the performance of a BCI system, and also provide elements for an analysis of the spectral content of EEG signals and of the performance of ELMs in motor imagery paradigms.

Journal ArticleDOI
TL;DR: The local network neighborhood A IS model is proposed as a network based AIS model which uses an index-based ALC neighborhood to determine the network connectivity between ALCs and the advantages of using these network topologies.
Abstract: The network theory in immunology inspired the modeling of network based artificial immune system (AIS) models for data clustering. Current network based AIS models determine the network connectivity between artificial lymphocytes (ALCs) by measuring the spatial distance between these ALCs against a distance threshold or by grouping ALCs into sub-networks. This paper discusses alternative network topologies to determine the network connectivity between ALCs and the advantages of using these network topologies. The local network neighborhood AIS model is then proposed as a network based AIS model which uses an index-based ALC neighborhood to determine the network connectivity between ALCs. The proposed model is compared to existing network based AIS models which are applied to data clustering problems. Furthermore, a sensitivity analysis is also done on the proposed model to investigate the influence of the model’s parameters on the quality of the clusters. The paper also gives a formal definition of data clustering and discusses the performance measures used to determine the quality of clusters.

Book ChapterDOI
01 Jan 2012
TL;DR: An immunity algorithm adapting capabilities of the immune system is proposed and enable robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle efficiency.
Abstract: Planning of the optimal path has always been the target pursued by many researchers since last five decade. Its application on mobile robot is one of the most important research topics among the scientist and researcher. This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle. An immunity algorithm adapting capabilities of the immune system is proposed and enable robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle efficiency. Finally, we have compared with the GA based path planning with the AIA based path planning. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively by using AIA than GA.

Journal ArticleDOI
TL;DR: Two major immunological principles presented in the literature are revisited: hypermutation, which is responsible for local search, and receptor edition, used to explore different areas in the solution space, and three major modifications are presented, divided into two different goals.
Abstract: The main objective of this paper is to use artificial immune systems (AIS) in optimization problems. For this purpose, two major immunological principles presented in the literature are revisited: hypermutation, which is responsible for local search, and receptor edition, used to explore different areas in the solution space. This paper presents three major modifications divided into two different goals. The first goal is to speed up the convergence of each individual. This is done through a new hypermutation approach that uses the numerical information provided by the optimization system to drive the cloning process to interesting directions into the solution space. The second goal regards the reduction of the computational effort necessary to simulate the whole population. This is accomplished by adding to the AIS algorithm two more features of the natural immune system: maturation control and memory cells. The maturation control analyzes the antibodies and, during the convergence process, eliminates possible redundancies, represented by individuals driving to the same local optimum. The last proposed improvement is the use of memory cells in dynamic-optimization scenarios. In such situations, a repertoire of successful cases is used to forecast part of the initial population. Combining these concepts together decreases the number of antibodies, generations, and clones, consequently speeding up the convergence process. Applications illustrate the performance of the proposed method.

Book ChapterDOI
30 Mar 2012
TL;DR: Improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data preprocessing that appear to be promising for online anomaly-based intrusion detection.
Abstract: As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Algorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data pre-processing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.

Proceedings ArticleDOI
13 Aug 2012
TL;DR: In this paper, a non-linear dynamic inversion approach augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response is presented.
Abstract: In this paper, a novel adaptive flight control system is presented, designed to handle failures and malfunctions of aircraft sub-systems as well as general environmental upset conditions. The proposed control laws use a non-linear dynamic inversion approach augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response. This work is an extension of a recently developed artificial immune system-based architecture which implements negative and positive selection algorithms for aircraft fault detection, identification, and evaluation within a hierarchical multi-self scheme. The effectiveness of the approach is demonstrated through simulation examples within the West Virginia University unmanned aerial vehicle simulation environment. The performance of the control laws is evaluated in terms of trajectory tracking errors and control activity during autonomous flight in the presence of atmospheric disturbances and actuator failures. The results show that the proposed fault tolerant adaptive control laws significantly improve the tracking performance of the vehicle at nominal conditions and under a variety of abnormal flight conditions.

Journal ArticleDOI
TL;DR: The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF-THEN rules.

Journal ArticleDOI
TL;DR: Simulation results on difficult test problems, both in combinatorial and continuous optimization, show that the proposed GISMOO algorithm is able to obtain better results than state of the art algorithms.

Proceedings ArticleDOI
10 Jun 2012
TL;DR: This paper proposes an improved version of the standard genetic algorithm approach by making use of the clonal selection principle from artificial immune systems and Experimental results show that theClonal selection based genetic algorithm achieves much higher fitness values for the workflow selection problem than standard Genetic algorithm.
Abstract: Quality of Service (QoS) aware service selection of workflows is a very important aspect for service-oriented systems. The selection based on QoS allows the user to include also non-functional attributes in their query, such as availability and reliability. Several exact methods have been proposed in the past, however, given that the workflow selection problem is NP-hard, approximate algorithms can be used to find suboptimal solutions for requested workflows. Genetic algorithm is one such method that can find approximate solutions in the form of services selected. In this paper, we propose an improved version of the standard genetic algorithm approach by making use of the clonal selection principle from artificial immune systems. Experimental results show that the clonal selection based genetic algorithm achieves much higher fitness values for the workflow selection problem than standard genetic algorithm.

Journal ArticleDOI
TL;DR: The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions.
Abstract: SUMMARY This work describes the development, implementation, and assessment of enhanced variants of three different groups of bio-inspired methodologies: genetic algorithms, particle swarm optimization, and artificial immune system. The algorithms are implemented on a computational tool for the synthesis and optimization of offshore oil production risers that connect a floating platform at the sea surface to the wellheads at the sea bottom. Optimization procedures using bio-inspired algorithms for such real-world engineering problems require the calculation of the objective function through a large number of time-consuming finite element nonlinear dynamic analyses, for the evaluation of the structural behavior of each candidate configuration. Therefore, the performance of the algorithms may be measured by the smaller number of objective function evaluations associated to a given target fitness value. The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions. Copyright © 2012 John Wiley & Sons, Ltd.

Book ChapterDOI
28 Aug 2012
TL;DR: It is proved that the B-cell algorithm outperforms these evolutionary algorithms by far and is another demonstration that relatively simple artificial immune systems can excel over more complex evolutionary algorithms in the domain of optimisation.
Abstract: Computing a longest common subsequence of a number of strings is a classical combinatorial optimisation problem with many applications in computer science and bioinformatics. It is a hard problem in the general case so that the use of heuristics is motivated. Evolutionary algorithms have been reported to be successful heuristics in practice but a theoretical analysis has proven that a large class of evolutionary algorithms using mutation and crossover fail to solve and even approximate the problem efficiently. This was done using hard instances. We reconsider the very same hard instances and prove that the B-cell algorithm outperforms these evolutionary algorithms by far. The advantage stems from the use of contiguous hypermutations. The result is another demonstration that relatively simple artificial immune systems can excel over more complex evolutionary algorithms in the domain of optimisation.