98 results
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2. Detection of Pipes Causing Pressure Loss in Water Distribution Networks via Artificial Immune Systems.
- Author
-
ERYİĞİT, Miraç
- Abstract
Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
3. Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management.
- Author
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Lo, Shih-Che and Chuang, Ying-Lin
- Subjects
CROSS-docking (Logistics) ,VEHICLE routing problem ,IMMUNE system ,BENCHMARK problems (Computer science) ,SUPPLY chain management - Abstract
Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers' demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers' requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Artificial Immune System-Based Learning Style Stereotypes.
- Author
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Sotiropoulos, Dionisios N., Alepis, Efthimios, Kabassi, Katerina, Virvou, Maria K., Tsihrintzis, George A., and Sakkopoulos, Evangelos
- Subjects
COGNITIVE styles ,SYNTHETIC antibodies ,K-means clustering ,STEREOTYPES ,PATTERN perception ,GENDER stereotypes ,REINFORCEMENT learning - Abstract
This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
5. Enhanced Artificial Immune Systems and Fuzzy Logic for Active Distribution Systems Reconfiguration.
- Author
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Alonso, Guillermo, Alonso, Ricardo F., De Souza, Antonio Carlos Zambroni Zambroni, and Freitas, Walmir
- Subjects
FUZZY systems ,IMMUNE system ,DISTRIBUTED power generation ,PROBLEM solving - Abstract
Nowadays, the high penetration of automation on smart grids challenges electricity companies in providing an efficient distribution networks operation. In this sense, distribution system reconfiguration (DSR) plays an important role since it may help solve real-time problems. This paper proposes a methodology to solve the DSR problem using artificial immune systems (AIS) based on a new, efficient, and robust approach. This new methodology, called Enhanced Artificial Immune Systems (EAIS), uses the values of the currents in wires for intelligent mutations. The problem is accomplished by a multi-objective optimization with fuzzy variables, minimizing power losses, voltage deviation, and feeders load balancing. A comparison with other DSR solution methods is presented. The method is compared with two other previously proposed methods with the help of the 33-bus, 84-bus, and 136-bus distribution systems. Different scenarios are analyzed, including the optimal location of the Distributed Generation (DG). The results show the applicability of the proposed algorithm for the simultaneous solution of DSR and location or dispatch of DGs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Optimal Design of Electrothermal Microactuators for Many Criteria by Means of an Immune Game Theory Multiobjective Algorithm.
- Author
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Długosz, Adam, Jarosz, Paweł, and Schlieter, Tomasz
- Subjects
MICROACTUATORS ,GAME theory ,FINITE element method ,SELF-organizing maps ,DIFFERENTIAL evolution ,S-matrix theory - Abstract
The paper presents the application of the IMGAMO (immune game theory multiobective algorithm) in the optimal design of electrothermal microactuators. Several numerical tests on the mathematical benchmark test functions were performed, showing the superiority of the IMGAMO, when a large number of criteria are considered, in comparison to other multiobjective optimizers. A parametric numerical model of an electrothermal microactuaror was developed and verified. Six functionals, which depend on various thermal and mechanical quantities of the microactuator, were proposed, formulated and numerically implemented. These functionals represent real requirements asked of microactuators. The boundary-value problem of an electro-thermo-mechanical field was solved multiple times during the course of optimization by way of the finite element method (FEM). A numerical example of multiobjective optimization of chevron-type electrothermal actuators is included in the paper. Representation of the multi-dimensional Pareto fronts by means of scatter plot matrices, aided by self-organizing maps (SOMs), is presented. The novel method of selecting interesting, compromise-solutions is proposed and described. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
7. An Artificial-Immune-System-Based Algorithm Enhanced with Deep Reinforcement Learning for Solving Returnable Transport Item Problems.
- Author
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Achamrah, Fatima Ezzahra, Riane, Fouad, Sahin, Evren, and Limbourg, Sabine
- Abstract
This paper proposes a new approach, i.e., virtual pooling, for optimising returnable transport item (RTI) flows in a two-level closed-loop supply chain. The supply chain comprises a set of suppliers delivering their products loaded on RTIs to a set of customers. RTIs are of various types. The objective is to model a deterministic, multi-supplier, multi-customer inventory routing problem with pickup and delivery of multi-RTI. The model includes inventory-level constraints, the availability of empty RTIs to suppliers, and the minimisation of the total cost, including inventory holding, screening, maintenance, transportation, sharing, and purchasing costs for new RTIs. Furthermore, suppliers with common customers coordinate to virtually pool their inventory of empty RTIs held by customers so that, when loaded RTIs are delivered to customers, each may benefit from this visit to pick up the empty RTI, regardless of the ownership. To handle the combinatorial complexity of the model, a new artificial-immune-system-based algorithm coupled with deep reinforcement learning is proposed. The algorithm combines artificial immune systems' strong global search ability and a strong self-adaptability ability into a goal-driven performance enhanced by deep reinforcement learning, all tailored to the suggested mathematical model. Computational experiments on randomly generated instances highlight the performance of the proposed approach. From a managerial point of view, the results stress that this new approach allows for economies of scale and cost reduction at the level of all involved parties to about 40%. In addition, a sensitivity analysis on the unit cost of transportation and the procurement of new RTIs is conducted, highlighting the benefits and limits of the proposed model compared to dedicated and physical pooling modes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection.
- Author
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González-Patiño, David, Villuendas-Rey, Yenny, Argüelles-Cruz, Amadeo José, Camacho-Nieto, Oscar, and Yáñez-Márquez, Cornelio
- Subjects
IMMUNOCOMPUTERS ,IMMUNE system ,BIOLOGICALLY inspired computing ,BREAST cancer ,SWARM intelligence ,CANCER diagnosis ,CLASSIFICATION algorithms - Abstract
Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. TheWilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
9. Detection and Classification of Voltage Disturbances in Electrical Power Systems Based on Multiresolution Analysis and Negative Selection Algorithm.
- Author
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Bernardes, Haislan and Minussi, Carlos Roberto
- Subjects
PATTERN recognition systems ,ELECTRIC power ,ARTIFICIAL neural networks ,ELECTRICAL energy ,WAVELET transforms - Abstract
Early detection of threats to electrical energy distribution systems helps professionals make decisions and mitigate interruptions in supply and improper activation of the protection system. Biologically inspired methods, e.g., artificial neural networks, genetic algorithms, and ant colonies, solve optimization problems and facilitate pattern recognition and decision-making. The present work presents a tool for detecting and classifying voltage disturbances based on the negative selection algorithm, which identifies and eliminates self-reactive cells, associated with multiresolution analysis, which analyzes the signal at different scales of detail, allowing a more complete understanding and detailed description of the phenomenon in question. The negative wavelet selection algorithm demonstrates robustness to detect and classify disturbances. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration.
- Author
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Alonso, F. R., Oliveira, D. Q., and Zambroni de Souza, A. C.
- Subjects
ELECTRIC power distribution ,ELECTRIC power systems research ,ELECTRIC potential ,REACTIVE power ,ELECTRIC power system control - 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 Administración Nacional de Electricidad (ANDE) real feeder (CBO-01 23-kV feeder). [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
11. A hybrid multi-objective AIS-based algorithm applied to simulation-based optimization of material handling system.
- Author
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Leung, Chris Siu Kei and Lau, Henry Ying Kei
- Subjects
MATERIALS handling ,IMMUNOCOMPUTERS ,MATHEMATICAL optimization ,GENETIC algorithms ,COMPUTER simulation - Abstract
Graphical abstract Material Handling System. Highlights • A hybrid multi-objective optimization algorithm derived from immunological and biological evolution concepts is proposed. • An optimization approach integrating the proposed algorithm with an industrial-grade simulator is implemented. • The optimization approach’s performance is examined via numerical benchmark problems and a real-life simulation study. • The results reveal its ability of helping management to find near optimal system operating conditions and parameters. Abstract Optimization of complex real-world problems often involves multiple objectives to be considered simultaneously. These objectives are often non-commensurable and competing. For example, material handling is a vital element of industrial processes, which involves a variety of operations including the movement, storage and control of materials throughout the processes of manufacturing, distribution, and disposal while having to satisfy multiple objectives. Having an efficient and effective material handling system (MHS) is of great importance to various industries, such as manufacturing and logistics industries, for maintaining and facilitating a continuous flow of materials through the workplace and guaranteeing that required materials are available when needed. In this paper, a hybrid multi-objective optimization algorithm largely based on Artificial Immune Systems (AIS) is applied to simulation-based optimization of material handling system. This proposed algorithm hybridizes the AIS with the Genetic Algorithm (GA) by incorporating the crossover operator derived from the biological evolution. The reason behind such hybridization is to further enhance the diversity of the clone population and the convergence of the algorithm. In this paper, other than conducting numerical experiments to assess the performance of the proposed algorithm by using several benchmark problems, the proposed algorithm is also applied to optimize a multi-objective simulation-based problem on a material handling system in order to demonstrate the applicability of the proposed algorithm in real-life cases. The results show that for most cases the proposed algorithm outperforms the other benchmark algorithms especially in terms of solution diversity. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
12. Artificial Immune Classifier Based on ELLipsoidal Regions (AICELL).
- Author
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Lanaridis, Aris, Siolas, Giorgos, and Stafylopatis, Andreas
- Subjects
PATTERN perception ,MACHINE learning ,LEARNING classifier systems ,EVOLUTIONARY algorithms ,MACHINE theory ,ALGORITHMS - Abstract
Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
13. Development and Practical Applications of Computational Intelligence Technology.
- Author
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Matsuzaka, Yasunari and Yashiro, Ryu
- Subjects
COMPUTATIONAL intelligence ,IMMUNOCOMPUTERS ,NATURAL language processing ,IMMUNE system ,REINFORCEMENT learning - Abstract
Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body's immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body's immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves "finding a solution that maximizes or minimizes an objective function under given constraints". In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Detection of Pipes Causing Pressure Loss in Water Distribution Networks via Artificial Immune Systems
- Author
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Mirac Eryiğit
- Subjects
basınç kaybı ,su dağıtım şebekeleri ,optimizasyon ,model kalibrasyonu ,yapay bağışıklık sistemleri ,pressure loss ,water distribution networks ,optimization ,model calibration ,artificial immune systems ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Science ,Science (General) ,Q1-390 - Abstract
This paper proposes the optimization model using Artificial Immune Systems, depending on a model calibration, in order to determine worn out pipes with low Hazen-Williams roughness coefficient causing pressure loss in the water distribution networks. The modified Clonal Selection Algorithm, a type of Artificial Immune Systems, was used as a heuristic optimization method. In order to evaluate its performance, the model was implemented to the four-loop hypothetical water distribution network under steady-state conditions. According to the results, the model appeared to be promising in the detection of old pipes causing high pressure losses in the water distribution networks
- Published
- 2023
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- View/download PDF
15. A T-cell algorithm for solving dynamic economic power dispatch problems.
- Author
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Aragón, Victoria S., Coello Coello, Carlos A., and Leguizamón, Mario G.
- Subjects
ALGORITHMS ,COMPUTER science conferences ,T cells ,IMMUNE system ,STATISTICS - Abstract
Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
16. Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms.
- Author
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Duma, Mlungisi and Twala, Bhekisipho
- Subjects
- *
FILTERING software , *IMMUNE system , *RECOMMENDER systems , *GENETIC algorithms , *NONNEGATIVE matrices , *FILTERS & filtration , *EXPERT systems - Abstract
• Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm. • Sustained and reliable predictions as the number of missing data increases in the datasets. • Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases. In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset. The effect is poor predictions and recommendations of items to users by expert and intelligent systems like the recommender system. This paper proposes a Nearest Neighbour Artificial Immune System with a Genetic Algorithm (NNAISGA) to perform fast data imputations to reduce sparseness. The main impact is sustained and reliable predictions as the number of missing data increases in the dataset. The second benefit is to help sustain or improve recommendations of items to users from common methods used in collaborative filtering such as the User-based, Item-based, Slope-one, Tendencies-based and Non-Negative Matrix Factorisation (NNMF) methods. We show that NNAISGA is the desired method for learning and imputation over the traditional genetic algorithm. The findings show that using the NNAISGA as a fast imputation method yields promising results. All of the methods, except for the User-based method, show significant improvements or sustained accuracies in terms of predictions and recommendations. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
17. Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm.
- Author
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Pinto, Carlos, Pinto, Rui, and Gonçalves, Gil
- Subjects
ANOMALY detection (Computer security) ,DENDRITIC cells ,ALGORITHMS ,OBJECT-oriented methods (Computer science) ,INTRUSION detection systems (Computer security) ,CYBER physical systems - Abstract
The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems' dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work's contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem.
- Author
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Corus, Dogan, Oliveto, Pietro S., and Yazdani, Donya
- Subjects
- *
IMMUNE system , *EVOLUTIONARY algorithms , *NP-hard problems , *APPROXIMATION algorithms , *COMBINATORIAL optimization - Abstract
Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been shown for artificial example functions constructed especially to show difficulties that EAs may encounter during the optimisation process. However, no evidence is available indicating that these two operators have similar behaviour also in more realistic problems. In this paper we perform an analysis for the standard NP-hard Partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time. As a result we prove that while EAs and random local search (RLS) may get trapped on 4/3 approximations, AISs find arbitrarily good approximate solutions of ratio (1 + ϵ) within n (ϵ − (2 / ϵ) − 1) (1 − ϵ) − 2 e 3 2 2 / ϵ + 2 n 3 2 2 / ϵ + 2 n 3 function evaluations in expectation. This expectation is polynomial in the problem size and exponential only in 1 / ϵ. To the best of our knowledge this is the first time performance guarantees of any AIS are proven for a classical combinatorial optimisation problem. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
19. A Novel Multi-Objective Immune Memetic Algorithm for the Frequency Assignment Problem.
- Author
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Bessedik, Malika, Benbouzid-SiTayeb, Fatima, Kiouche, Abd Errahmane, and Keddar, Mohamed Reda
- Subjects
ASSIGNMENT problems (Programming) ,EVOLUTIONARY algorithms ,DIFFERENTIAL evolution ,ALGORITHMS ,EVOLUTIONARY computation ,IMMUNE system - Abstract
This paper presents a multi-objective immune memetic algorithm to the challenge of solving the Frequency Assignment Problem (FAP) in cellular networks seeking the minimization of the network's total interference, the maximum interference and the number of used frequencies. The originality of the proposed approach lies in integrating a FAP-specific local search into its evolutionary process instead of crossover and mutation, as well as a guided diversification strategy for better performances. Moreover, the algorithm is supplemented with a clonal selection, inherited from Artificial Immune Systems (AIS), which aims to improve the algorithm exploration and exploitation abilities. Computational experiments performed over COST259 instances show the efficiency of the newly proposed evolutionary multi-objective algorithm and corroborated by the comparisons we did with the most frequently referred algorithm in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
20. Implementing sequence-based antigenic distance calculation into immunological shape space model
- Author
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Anderson, Christopher S., Sangster, Mark Y., Yang, Hongmei, Mariani, Thomas J., Chaudhury, Sidhartha, and Topham, David J.
- Published
- 2020
- Full Text
- View/download PDF
21. Optimal Design of Electrothermal Microactuators for Many Criteria by Means of an Immune Game Theory Multiobjective Algorithm
- Author
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Adam Długosz, Paweł Jarosz, and Tomasz Schlieter
- Subjects
optimal design ,multiobjective optimization ,mems ,microthermal actuators ,fem ,artificial immune systems ,game theory ,self organizing maps ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
The paper presents the application of the IMGAMO (immune game theory multiobective algorithm) in the optimal design of electrothermal microactuators. Several numerical tests on the mathematical benchmark test functions were performed, showing the superiority of the IMGAMO, when a large number of criteria are considered, in comparison to other multiobjective optimizers. A parametric numerical model of an electrothermal microactuaror was developed and verified. Six functionals, which depend on various thermal and mechanical quantities of the microactuator, were proposed, formulated and numerically implemented. These functionals represent real requirements asked of microactuators. The boundary-value problem of an electro-thermo-mechanical field was solved multiple times during the course of optimization by way of the finite element method (FEM). A numerical example of multiobjective optimization of chevron-type electrothermal actuators is included in the paper. Representation of the multi-dimensional Pareto fronts by means of scatter plot matrices, aided by self-organizing maps (SOMs), is presented. The novel method of selecting interesting, compromise-solutions is proposed and described.
- Published
- 2019
- Full Text
- View/download PDF
22. Comparison of classification techniques applied for network intrusion detection and classification.
- Author
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A. Aziz, Amira Sayed, Hanafi, Sanaa EL-Ola, and Hassanien, Aboul Ella
- Subjects
COMPUTER network resources ,INTRUSION detection systems (Computer security) ,IMMUNOCOMPUTERS ,MULTIAGENT systems ,NAIVE Bayes classification - Abstract
In a previous research, a multi-agent artificial immune system for network intrusion detection and classification was proposed and tested, where a multi-layer detection and classification process was executed on each agent, for each host in the network. In this paper, we show the experiments that were held to chose the appropriate classifiers by testing different classifiers and comparing them to increase the detection accuracy and obtain more information on the detected anomalies. It will be shown that no single classifier should be used for all types of attacks, due to different classification rates obtained. This is due to attacks representations in the train set and dependency between features used to detect them. It will also be shown that a basic and simple classifier such as Naive Bayes has better classification results in the case of low-represented attacks, and the basic decision trees such as Naive-Bayes Tree and Best-First Tree give very good results compared to well-known J48 (Weka implementation of C4.5) and Random Forest decision trees. Based on these experiments and their results, Naive Bayes and Best-First tree classifiers were selected to classify the anomaly-detected traffic. It was shown that in the detection phase, 90% of anomalies were detected, and in the classification phase, 88% of false positives were successfully labeled as normal traffic connections, and 79% of DoS and Probe attacks were labeled correctly, mostly by NB, NBTree, and BFTree classifiers. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
23. NDAMM: a numerical differentiation-based artificial macrophage model for anomaly detection
- Author
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Ming, Zhe, Liang, Yiwen, and Zhou, Wen
- Published
- 2023
- Full Text
- View/download PDF
24. Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study.
- Author
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Costa Silva, Guilherme, Caminhas, Walmir Matos, and Palhares, Reinaldo Martinez
- Subjects
IMMUNOCOMPUTERS ,BIOLOGICALLY inspired computing ,IMMUNE system ,ANOMALY detection (Computer security) ,FAULT diagnosis - Abstract
This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
25. Artificial Immune System-Based Classification in Extremely Imbalanced Classification Problems.
- Author
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Sotiropoulos, Dionisios N. and Tsihrintzis, George A.
- Subjects
- *
IMMUNOCOMPUTERS , *MACHINE learning , *SUPPORT vector machines , *MULTILAYER perceptrons , *PATTERN recognition systems - Abstract
This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced 'self' / 'non-self' discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the 'non-self' space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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26. Immunological algorithms paradigm for construction of Boolean functions with good cryptographic properties.
- Author
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Picek, Stjepan, Sisejkovic, Dominik, and Jakobovic, Domagoj
- Subjects
- *
BOOLEAN functions , *CRYPTOGRAPHY , *GENETIC algorithms , *COMBINATORIAL optimization , *EVOLUTIONARY algorithms - Abstract
In this paper we investigate the efficiency of two immunological algorithms (CLONALG and opt-IA) in the evolution of Boolean functions suitable for use in cryptography. Although in its nature a combinatorial problem, we experiment with two representations of solutions, namely, the bitstring and the floating point based representation. The immunological algorithms are compared with two commonly used evolutionary algorithms – genetic algorithm and evolution strategy. To thoroughly investigate these algorithms and representations, we use four different fitness functions that differ in the number of parameters and difficulty. Our results indicate that for smaller dimensions immunological algorithms behave comparable with evolutionary algorithms, while for the larger dimensions their performance is somewhat worse. When considering only immunological algorithms, opt-IA outperforms CLONALG in most of the experiments. The difference in the representation for those algorithms is also clear where floating point works better with smaller problem sizes and bitstring representation works better for larger Boolean functions. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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27. A new synthetic feature generation scheme based on artificial immune systems for robust offline signature verification.
- Author
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Arab, Naouel, Nemmour, Hassiba, and Chibani, Youcef
- Subjects
- *
IMMUNE system , *DATA augmentation , *DESCRIPTOR systems - Abstract
• Offline Handwritten signature verification systems suffer from the lack of genuine signature samples. • We propose a data augmentation algorithm that generates synthetic genuine signature features. • We propose a new signature shape descriptor based on multi-oriented local difference features. • Results obtained on benchmark datasets confirm the effectiveness of proposed methods. Identity verification based on offline handwritten signatures is required in various financial and administrative domains using paper documents. Commonly, the performance of signature verification systems is altered by the lack of sufficient authentic signatures. To overcome such shortcoming, various data augmentation schemes were proposed to generate synthetic samples. Since both genuine and synthetic signatures undergo a feature generation process inside the verification system, we propose to directly apply data augmentation on features instead of the signature image. Such an approach can give more pertinent synthetic features, than those derived from synthetic signatures. The proposed features generator is based on mutation, cloning, and resources competition mechanisms of artificial immune systems. It is evaluated on two descriptors by using SVM classifier in the verification step. As a first descriptor, we introduce the multi-oriented local difference features. The second descriptor is the histogram of templates which is used for comparison aims, since it showed satisfactory performance for handwritten signature characterization. Experiments conducted on CEDAR, GPDS-300, and MCYT-75 datasets, highlight the effectiveness of synthetic features, which allow at least an improvement of 8% when the verification system is developed by using one real signature. Also, the proposed MLDF outperforms both HOT and several state of the art features including LBP and SigNet. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. On the effectiveness of immune inspired mutation operators in some discrete optimization problems.
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Xia, Xiaoyun and Zhou, Yuren
- Subjects
- *
IMMUNOCOMPUTERS , *OPERATOR theory , *HEURISTIC algorithms , *DISCRETE systems , *MATHEMATICAL optimization - Abstract
Artificial immune systems have been widely applied to a variety of complex real-world problems. However, theoretical studies on artificial immune system are still limited and there is a strong need for building a rigorous theoretical foundation to better understand these heuristics. This paper contributes to a theoretical runtime analysis of immune inspired hypermutations on some discrete optimization problems. In particular, we are interested in the performance comparison among somatic contiguous hypermutations (CHM), standard bit mutations (SBM) and local mutation. We reveal that the immune inspired hypermutations can significantly outperform the standard bit mutation most often used in evolutionary algorithms on some well-known pseudo-Boolean functions including Trap and Hierarchical-if-and-only-if functions and instances of two combinatorial optimization problems, namely the Max-Cut problem and the Minimum s-t-cut problem. The proofs give some insights into the relationships between the problem characteristics and algorithmic features. The results of the analysis help strengthen the usefulness of Artificial immune systems. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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29. Prognosis and fail detection in a dynamic rotor using artificial immunological system
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Fábio Roberto Chavarette, Vishnu Narayan Mishra, Aparecido Carlos Gonçalves, Roberto Outa, Thiago Carreta Moro, Luiz Gustavo Pereira Roéfero, FATEC Aracatuba, Universidade Estadual Paulista (Unesp), and Indira Gandhi Natl Tribal Univ
- Subjects
Structure (mathematical logic) ,0209 industrial biotechnology ,Structural health monitoring ,Computer science ,Artificial immune system ,General Engineering ,68W40 ,Computational intelligence ,02 engineering and technology ,Fault detection and isolation ,Field (computer science) ,Computer Science Applications ,020901 industrial engineering & automation ,Computational Theory and Mathematics ,Risk analysis (engineering) ,Negative selection algorithm ,Pattern recognition (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,42C40 ,Software ,Reliability (statistics) ,Artificial immune systems - Abstract
Made available in DSpace on 2020-12-10T19:55:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-20 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Cientifico e Tecnol ~ogico Purpose In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people's lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM). Design/methodology/approach The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection. Findings The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested. Originality/value This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems - a topic not yet addressed in the literature. FATEC Aracatuba, Fac Technol Aracatuba, Dept Biofuels, Aracatuba, Brazil Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Math, Ilha Solteira, Brazil Indira Gandhi Natl Tribal Univ, Dept Math, Amarkantak, India Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Mech Engn, Ilha Solteira, Brazil Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Math, Ilha Solteira, Brazil Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Mech Engn, Ilha Solteira, Brazil FAPESP: 2019/10515-4 Conselho Nacional de Desenvolvimento Cientifico e Tecnol ~ogico: 312972/2019-9
- Published
- 2020
30. Hardware Immune System for Embedded IoT.
- Author
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Zareen, Farhath, Amador, Mateus Augusto Fernandes, and Karam, Robert
- Abstract
Malware is a major threat to present-day computing systems. With the rapid growth of Internet of Things (IoT) devices and their usage in safety critical systems, security has become increasingly important. Securing IoT devices is a challenge for designers, as they are generally resource constrained, which makes real-time software-based malware detection difficult or infeasible. A promising alternative approach is to utilize intrinsic hardware-based malware detectors to alleviate power and performance overheads. In this brief, we introduce a novel Hardware Immune System (HWIS), a stand-alone, hardware-supported malware detection approach for microprocessors that leverages Artificial Immune Systems for detecting botnet activity. This technique is intended for low-power, resource constrained and network facing embedded devices. The proposed model is capable of detecting botnet behavior with an accuracy of 96.7% and F1-score of 0.96. The technique is implemented in hardware and verified using Spartan-7 FPGA. Our technique achieves power, LUTs, FFs, DSPs, and BRAMs utilization overheads of 0.6%, 8.5%, 11.8%, 0%, and 0%, respectively, with no impact on delay using the RISC-V CPU as a baseline. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
31. An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems.
- Author
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Lima, Fernando P.A., Lopes, Mara L.M., Lotufo, Anna Diva P., and Minussi, Carlos R.
- Subjects
- *
IMMUNOCOMPUTERS , *ELECTRIC potential , *ELECTRIC power distribution , *MACHINE learning , *PATTERN recognition systems - Abstract
This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
32. Application of Clonal Selection Algorithm in Construction Site Utilization Planning Optimization.
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Wang, Xi, Deshpande, Abhijeet S., Dadi, Gabriel B., and Salman, Baris
- Subjects
CLONAL selection algorithms ,BUILDING sites ,CONSTRUCTION project management ,DECISION making ,MATHEMATICAL optimization - Abstract
Various algorithms inspired by evolutionary and physical processes have been extensively applied in solving complex construction engineering optimization problems. In this paper, Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, is introduced in terms of its basic mechanisms and its applications in construction engineering. Specifically, Clonal Selection Algorithm (CSA), one of main algorithms that form AIS, is based on clonal selection process of the immune system which includes the selection, hypermutation, and receptor editing processes. We discuss the CSA in detail and present its application in the classic construction optimization problem, construction site utilization planning (CSUP), which is the decision making process for identifying the most optimal layout of temporary facilities designed to support the construction process. When applied to a test case published in research literature, we found that CSA shows a robust capacity to search the solution space effectively and efficiently. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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- View/download PDF
33. Fault detection, diagnosis and recovery using Artificial Immune Systems: A review.
- Author
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Bayar, Nawel, Darmoul, Saber, Hajri-Gabouj, Sonia, and Pierreval, Henri
- Subjects
- *
IMMUNOCOMPUTERS , *FAULT-tolerant computing , *COMPUTER architecture , *ARTIFICIAL intelligence , *BIOMECHANICS - Abstract
Biological immunity is a natural system that protects a host organism against disease causing elements threatening its normal functioning. It offers many interesting features that inspired the design of Artificial Immune Systems (AIS) to solve several kinds of engineering problems. As a manufacturing system can be assimilated to a host organism, while process anomalies (e.g. faults, errors and failures) can be considered as disease causing elements, biological immunity is particularly inspiring approaches for fault detection, diagnosis and recovery (FDDR). Although many interesting works and different adaptations were suggested, we are not aware of any recent survey that would aim at reviewing works, synthesizing modeling approaches and reporting on results in this field. This paper provides a recent survey and an analysis framework to fill in this gap. After a first part overviewing FDDR needs and requirements, we introduce biological immunity and highlight the main concepts and mechanisms that are particularly relevant to FDDR problems. The numerous works analyzed distinguish three categories of AIS: one-signal (for positive and negative selection) based approaches, two-signal (for danger and NK) based approaches and immune network based approaches. We suggest a possible architecture for FDDR systems, and organize the immune system concepts, components and mechanisms in such a way to show how they are applied for each of the detection, diagnosis and recovery tasks. Our analysis allows an overview of current technical and methodical developments in this field and foresight of future research perspectives. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
34. Immune Algorithm for Solving the Smooth Economic Dispatch Problem.
- Author
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Aragón, Victoria S. and Esquivel, Susana C.
- Subjects
IMMUNOCOMPUTERS ,METAHEURISTIC algorithms ,ALGORITHMS - Abstract
In this paper, an algorithm inspired on the T-Cell model of the immune system is presented, it is used to solve Economic Dispatch Problems with smooth objective function. The proposed approach is called IA EDP S, which stands for Immune Algorithm for Economic Dispatch Problem for smooth objective function, and it uses as differentiation process a redistribution power operator. The proposed approach is validated using five problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2015
35. Improved fault diagnosis algorithm based on artificial immune network model and neighbourhood rough set theory
- Author
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Yonghuang Zheng, Benhong Li, and Shangmin Zhang
- Subjects
fault diagnosis ,artificial immune systems ,rough set theory ,Computer engineering. Computer hardware ,TK7885-7895 ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low.
- Published
- 2021
- Full Text
- View/download PDF
36. Wavelet‐artificial immune system algorithm applied to voltage disturbance diagnosis in electrical distribution systems.
- Author
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Lima, Fernando P.A., Lotufo, Anna Diva P., and Minussi, Carlos Roberto
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
37. Self-adaptive attribute weighting for Naive Bayes classification.
- Author
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Wu, Jia, Pan, Shirui, Zhu, Xingquan, Cai, Zhihua, Zhang, Peng, and Zhang, Chengqi
- Subjects
- *
SELF-adaptive software , *NAIVE Bayes classification , *MACHINE learning , *ACCURACY , *BIG data , *PROBABILITY theory - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
38. QML-AiNet: An immune network approach to learning qualitative differential equation models.
- Author
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Pang, Wei and Coghill, George M.
- Subjects
IDIOTYPIC networks ,MACHINE learning ,DIFFERENTIAL equations ,SCALABILITY ,COMPUTER algorithms - Abstract
In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
39. Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting.
- Author
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Gálvez, Akemi, Iglesias, Andrés, Avila, Andreina, Otero, César, Arias, Rubén, and Manchado, Cristina
- Subjects
CLONAL selection theory ,COMPUTER algorithms ,KNOT theory ,SPLINE theory ,DATA analysis ,CAD/CAM systems ,VIRTUAL reality - Abstract
Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
40. A novel model for credit card fraud detection using Artificial Immune Systems.
- Author
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Soltani Halvaiee, Neda and Akbari, Mohammad Kazem
- Subjects
MATHEMATICAL models ,CREDIT card fraud ,SYSTEMS design ,AIS (Information retrieval system) ,INFORMATION theory - Abstract
The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
41. Application of Artificial Immune Systems in Advanced Manufacturing
- Author
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Rui Pinto and Gil Gonçalves
- Subjects
Artificial Immune Systems ,Autonomic Computing ,Advanced Manufacturing Systems ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
In recent years, the application of Advanced Manufacturing Technologies (AMT) in industrial processes represents the introduction of different Advanced Manufacturing Systems (AMS), which encourage enterprises to improve their core competitiveness and maintain sustainable development when facing the increasing demand for personalized product customization. More recently, AMT led to a new Internet revolution, mostly known as 4th Industrial Revolution. Considering the development and deployment of Artificial Intelligence to enable smart and self-behaving industrial systems, autonomic approaches allow the system to adapt itself, eliminating the need for human intervention for management. This paper presents a systematic literature review regarding Artificial Immune Systems (AIS) approaches to tackle multiple AMS problems requiring levels of autonomy. First, a systematic review of current industrial AIS applications in manufacturing environments is presented. Then, a conceptual framework is proposed to bridge the gap between research in the AIS field and the manufacturing industry while discussing key challenges and opportunities to be addressed by future research. This study aims to build a body of knowledge for researchers and manufacturers regarding AIS solutions under Advanced Manufacturing while suggesting directions for understanding the requirements for designing and managing autonomic industry applications supported by AIS.
- Published
- 2022
- Full Text
- View/download PDF
42. Innate immune memory and its application to artificial immune systems.
- Author
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Wang, Dongmei, Liang, Yiwen, Dong, Hongbin, Tan, Chengyu, Xiao, Zhenhua, and Liu, Sai
- Subjects
IMMUNOLOGIC memory ,IMMUNE system ,IMMUNOLOGISTS ,NATURAL immunity ,TOLL-like receptors ,DENDRITIC cells - Abstract
The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called ‘Trained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems.
- Author
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Samigulina, Galina and Samigulina, Zarina
- Subjects
IMMUNE system ,ALGORITHMS ,FAILURE mode & effects analysis ,INDUSTRIAL equipment ,FORECASTING ,FAULT location (Engineering) ,FINANCIAL risk ,PRODUCTION planning - Abstract
Nowadays, industrial enterprises are equipped with sophisticated equipment, diagnostics and prediction of the state of which is an urgent task. The article presents the developed system for diagnostics of industrial equipment based on the methodology for analyzing failure modes, their influence and the degree of AMDEC criticality (l'Analyse des Modes de Défaillances, de leurs Effets et de leur Criticité), as well as modified algorithms of artificial immune systems (AIS) on the example of real production data of TengizChevroil enterprise. The classical AMDEC model is improved by assessing the degree of criticality of equipment failures using the developed modified GWO-AIS and FPA-AIS algorithms based on gray wolf optimization and flower pollination methods. The proposed diagnostic system allows to reduce the financial risks of an enterprise associated with equipment faults by predicting possible failures, the possibility of planning maintenance, reducing the time for equipment repair and increasing the reliability of production. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Enabling data-driven anomaly detection by design in cyber-physical production systems.
- Author
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Pinto, Rui, Gonçalves, Gil, Delsing, Jerker, and Tovar, Eduardo
- Subjects
CYBER physical systems ,ANOMALY detection (Computer security) ,DENIAL of service attacks ,DENDRITIC cells ,INDUSTRIAL security ,MAINTAINABILITY (Engineering) - Abstract
Designing and developing distributed cyber-physical production systems (CPPS) is a time-consuming, complex, and error-prone process. These systems are typically heterogeneous, i.e., they consist of multiple components implemented with different languages and development tools. One of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system's maintainability. Adopting the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design, deployment, and management of CPPS in a model-based engineering methodology. We propose a method for CPPS design based on the IEC 61499 standard. The method allows designers to embed a bio-inspired anomaly-based host intrusion detection system (A-HIDS) in Edge devices. This A-HIDS is based on the incremental Dendritic Cell Algorithm (iDCA) and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS' Edge layer. This study's findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions, and cost-effective security by design system implementation. According to the experimental data, the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. A new artificial immune system based on continuous learning for pattern recognition
- Author
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Fábio Roberto Chavarette, Simone Silva Frutuoso de Souza, Fernando Parra dos Anjos Lima, State University of Mato Grosso (UNEMAT), Advanced Campus of Tangará da Serra, Universidade Estadual Paulista (Unesp), and FAPESP (Proc. n. 2019/10515-4) e CNPq (Proc. n. 312972/2019-9)
- Subjects
Negative selection algorithm ,Pattern Recognition ,Continuous Learning ,Artificial Immune Systems ,Negative Selection Algorithm ,Clonal Selection Algorithm ,General Computer Science ,Artificial immune system ,business.industry ,Computer science ,Pattern recognition ,Continuous training ,Artificial Inteligence ,Clonal selection algorithm ,Knowledge extraction ,Facial expression recognition ,Robustness (computer science) ,Artificial intelligence ,business ,Character recognition - Abstract
Made available in DSpace on 2021-06-25T10:49:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This paper presents a novel approach for pattern recognition based on continuous training inspired by the biological immune system operation. The main objective of this paper is to present a method capable of continually learn, i.e., being able to address new types of patterns without the need to restart the training process (artificial immune system with incremental learning). It is a useful method for solving problems involving a permanent knowledge extraction, e.g., 3D facial expression recognition, whose quality of the solutions is strongly dependent on a continuous training process. In this context, two artificial immune algorithms are employed: (1) the negative selection algorithm, which is responsible for the pattern recognition process and (2) the clonal selection algorithm, which is responsible for the learning process. The main application of this method is in assisting in decision-making on problems related to pattern recognition process. To evaluate and validate the efficiency of this method, the system has been tested on handwritten character recognition, which is a classic problem in the literature. The results show efficiency, accuracy and robustness of the proposed methodology. State University of Mato Grosso (UNEMAT), Campus of Tangará da Serra, Rodovia MT-358, Km 07, Jardim Aeroporto Federal Institute of Science and Technology Education of Mato Grosso (IFMT) Advanced Campus of Tangará da Serra, Rua 28, 980 N, Vila Horizonte Mathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31 Mathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31 FAPESP: 2019/10515-4 CNPq: 312972/2019-9
- Published
- 2020
46. A Bio-Inspired Reaction Against Cyberattacks: AIS-Powered Optimal Countermeasures Selection
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Pantaleone Nespoli, Felix Gomez Marmol, and Jorge Maestre Vidal
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Countermeasure selection ,cyberattack countermeasures ,intrusion reaction systems ,artificial immune systems ,bio-inspired reaction ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Nowadays, Information and Communication Technology (ICT) infrastructures play a crucial role for human beings, providing essential services at astonishing speed. Nevertheless, such a centrality of those infrastructures attracts the interest of ill-motivated actors that target such infrastructures with cyberattacks that are every day more sophisticated and more disruptive. In this alarming context, selecting the optimal set of countermeasures represents a primary need to react against the appearance of potentially dangerous threats effectively. With the motivation to contribute to develop ing faster and more effective response capabilities against them, the paper at hand introduces a novel cybersecurity reaction methodology based on Artificial Immune Systems (AIS), for which the evolutionary computing paradigm has been adopted. By leveraging the outstanding properties of these bio-inspired techniques, the selected countermeasures to defeat cyberthreats through cloning and mutation phases in an effort to improve the quality of the solution from a quantitative perspective, being able to adjust the risk to which the assets of the protected system are exposed. Exhaustive experiments demonstrate the feasibility of the proposed approach, reducing the risk in a more than acceptable time lapse.
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- 2021
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47. Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management
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Shih-Che Lo and Ying-Lin Chuang
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logistics management ,artificial immune systems ,vehicle routing problem ,cross-docking ,Mathematics ,QA1-939 - Abstract
Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test.
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- 2023
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48. Enhanced Artificial Immune Systems and Fuzzy Logic for Active Distribution Systems Reconfiguration
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Guillermo Alonso, Ricardo F. Alonso, Antonio Carlos Zambroni Zambroni De Souza, and Walmir Freitas
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distribution system reconfiguration ,artificial immune systems ,fuzzy logic ,distributed generation ,Technology - Abstract
Nowadays, the high penetration of automation on smart grids challenges electricity companies in providing an efficient distribution networks operation. In this sense, distribution system reconfiguration (DSR) plays an important role since it may help solve real-time problems. This paper proposes a methodology to solve the DSR problem using artificial immune systems (AIS) based on a new, efficient, and robust approach. This new methodology, called Enhanced Artificial Immune Systems (EAIS), uses the values of the currents in wires for intelligent mutations. The problem is accomplished by a multi-objective optimization with fuzzy variables, minimizing power losses, voltage deviation, and feeders load balancing. A comparison with other DSR solution methods is presented. The method is compared with two other previously proposed methods with the help of the 33-bus, 84-bus, and 136-bus distribution systems. Different scenarios are analyzed, including the optimal location of the Distributed Generation (DG). The results show the applicability of the proposed algorithm for the simultaneous solution of DSR and location or dispatch of DGs.
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- 2022
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49. Proposing a Features Preprocessing Method Based on Artificial Immune and Minimum Classification Errors Methods
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S. Rasoolzadeh, M. Majidi, and M. Miralvand
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Measure (data warehouse) ,Optimization problem ,Computer science ,Artificial immune system ,business.industry ,Evolutionary algorithm ,Evolutionary Algorithm ,General Engineering ,Pattern recognition ,Variance (accounting) ,Artificial immune algorithm ,Machine learning ,computer.software_genre ,Classification rate ,Optimization Problem ,Preprocessor ,Artificial intelligence ,business ,computer ,Artificial Immune Systems - Abstract
Artificial immune systems that have been inspired from organic immune systems, have drawn many attentions in recent years (and have been considered) as an evolutionary algorithm, and have been applied in different papers. This algorithm can be used in two different areas of optimization and classification. In this paper, an artificial immune algorithm has been applied in optimization problem. In particular, artificial immune systems have been used for computing the mapping matrices and improving features. Comparison of results of proposed method with other preprocessing methods shows the superiority of the proposed method so that in 90% of cases it has the best performance based on different measures. Evaluation measures are including classification rate, variance and compression measure.
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- 2015
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50. A T-cell algorithm for solving dynamic economic power dispatch problems
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Victoria Aragón, Carlos A. Coello Coello, and Mario A. Leguizamón
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artificial immune systems ,dynamic economic dispatch problem ,dynamic economic emission dispatch problem ,metaheuristics ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
This paper presents the artificial immune system IA_DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA\DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.
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- 2020
- Full Text
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