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A full-featured cooperative coevolutionary memory-based artificial immune system for dynamic optimization

APPLIED SOFT COMPUTING 2022年第0期117卷 108389-108389页

作者： Etaati, Bahareh Ghorrati, Zahra Ebadzadeh, Mohammad Mehdi Amirkabir Univ Technol Dept Comp Engn & Informat Technol 424 Hafez Ave Tehran 15914 Iran Purdue Univ Comp & Informat Technol M2M Lab W Lafayette IN 47907 USA

In this paper, a novel cooperative coevolutionary memory-based artificial immune system enhanced by a new clonal selection algorithm is proposed for dynamic optimization problems. In the proposed algorithm, the whole n-dimensional population is decomposed into n one-dimensional subpopulations. Then, each subpopulation is evaluated separately using a set of context vectors called short-term memory. Also, inspired by the production of new cells in bone marrow, each subpopulation is divided into multiple regions to track and locate multiple optima cooperatively. This division helps the algorithm exploit search space effectively. Additionally, inspired by the immune memory concept, a memory-based approach called long-term memory is proposed to store and retrieve essential information when a fitness change occurs. Furthermore, a new clonal selection method, a combination of negative selection and clonal selection mechanisms, is proposed. This proposed algorithm is faster than the basic clonal selection algorithm. Finally, compared to other immune-based algorithms, which usually are implemented based on one or two qualities of the biologic immune system, the proposed approach exploit almost all immune qualities. Several experiments are conducted on different configurations of the moving peaks benchmark to examine the efficiency of the proposed method. The experimental results confirm that the proposed method is competitive with other state-of-the-art algorithms to optimize dynamic problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： clonal selection algorithm Negative selection mechanism Cooperative approach Artificial immune system Dynamic optimization

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Reconstruction of the Gene Regulatory Network by Hybrid algorithm of clonal selection and Trigonometric Differential Evolution 38

Reconstruction of the Gene Regulatory Network by Hybrid Algo...

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IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

作者： Fefelov, A. Lytvynenko, V. Voronenko, M. Babichev, S. Osypenko, V. Kherson Natl Tech Univ Kherson Ukraine Jan Evangelista Purkyne Univ Usti Nad Labem Usti Nad Labem Czech Republic Natl Acad Sci Ukraine Inst Hydrobiol Kiev Ukraine

ISBN: (纸本)9781538663837

One of the ways to solve the problem with identifying parameters of S-system, which is used as a model for the reconstruction of a gene regulatory network, is considered. A hybrid algorithm based on a combination of clonal selection methods and trigonometric differential evolution has been proposed. The experimental investigations of the individual parameters influence of the hybrid algorithm on a level of model errors of time series approximation of gene expression data have been carried out. The results of comparative tests with other computational methods are presented.

关键词： gene regulatory networks reverse engineering gene expression ordinary differential equations s-system clonal selection algorithm trigonometric differential evolution

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C-NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

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IET INFORMATION SECURITY 2020年第6期14卷 683-693页

作者： Dandil, Emre Bilecik Seyh Edebali Univ Fac Engn Dept Comp Engn Bilecik Turkey

Security vulnerabilities in web traffic can directly lead to data leak. Preventing these data leaks to a large extent has become an important problem to solve. Besides, the accurate detection and prevention of abnormal changes in web traffic is of great importance. In this study, a hybrid approach, called C-NSA, based on the negative selection algorithm (NSA) and clonal selection algorithm (CSA) of artificial immune systems for the detection of abnormal web traffic on the network is proposed and a user-friendly application software is developed. The real and synthetic data in the Yahoo Webscope S5 dataset are used for web traffic and the data are split into windows using the window sliding. In the experimental studies, the abnormal web traffic data is detected by monitoring the changes in the number of activated detectors in the C-NSA. It is observed that the average accuracy performance of finding anomalies in real web traffic data is 94.30% and the overall classification accuracy is 98.22% based on proposed approach. In addition, false positive rate of the proposed approach using C-NSA is obtained as 0.029. In addition, the results in synthetic web traffic data using C-NSA are achieved as average 98.57% classification accuracy.

关键词： pattern classification information filtering Internet security of data telecommunication traffic time series artificial immune systems C-NSA hybrid approach artificial immune algorithms shared data data leak rapid detection clonal selection algorithm abnormal web traffic abnormal traffic data synthetic web traffic data

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Regional-Centralized Content Dissemination for eV2X Services in 5G mmWave-Enabled IoV

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IEEE INTERNET OF THINGS JOURNAL 2020年第8期7卷 7234-7249页

作者： Hu, Jinna Chen, Chen Qiu, Tie Pei, Qingqi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China

The fifth-generation (5G) mobile communication systems support the millimeter-wave (mmWave) communications, which enable content dissemination for enhanced V2X (eV2X) services. However, the current content dissemination architectures face various problems, such as limited radio coverage of mmWave communications and differentiated Quality-of-Experience (QoE) requirements of users. To address these, we propose a regional-centralized content dissemination (RC-CD) scheme for eV2X services. The RC-CD can deal with the requests from distributed vehicles by the regional-centralized service architecture. By introducing vehicle-to-vehicle and vehicle-to-infrastructure mmWave communications, the content dissemination services are expanded to the areas that the mmWave base stations cannot cover. According to different QoE requirements, the requests of eV2X services are categorized into two groups: 1) elastic requests and 2) inelastic requests. Our algorithm considers the channel characteristics of mmWave and the different QoE requirements of requests to jointly optimize the waiting time of elastic requests and the failure ratio of inelastic requests. It also adopts an efficient heuristic approximate algorithm to solve the NP-completed optimization problem. The performance of our proposed scheme is evaluated by simulations in a realistic city layout. The results show that our scheme can provide an efficient architecture for eV2X content dissemination that supports different types of requests, provides a better QoE for the users, and achieves broader service coverage.

关键词： 5G mobile communication Vehicle-to-everything Millimeter wave communication Quality of experience Internet of Things Computer architecture Reliability clonal selection algorithm content dissemination enhanced V2X (eV2X) applications Internet of Vehicles (IoV) millimeter-wave (mmWave) communication Quality of Experience (QoE)

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A clonal selection algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

A Clonal Selection Algorithm with Levenshtein Distance based...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Takumi Ichimura Shin Kamada Faculty of Management and Information Systems Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan Graduate School of Comprehensive Scientific Research Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan

ISBN: (纸本)9781479906505

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and clonal selection algorithm with Immunological Memory Cells in this paper.

关键词： Levenshtein Distance clonal selection algorithm Image Analysis Immunological Memory Cells Growing Hierarchical SOM Interactive GHSOM Smartphone based Participatory Sensing System Tourist Informatics Knowledge Discovery Levenshtein distance data mining image analysis images selection algorithm Tourist information services clonal selection Interactive Device Sensor CFC1 gene DATA ACQUISITION SYSTEMS

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Fault Detection and Isolation Based on Artificial Immune System for Injection Molding Machine

Fault Detection and Isolation Based on Artificial Immune Sys...

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Chinese Automation Congress (CAC)

作者： Ren, Yanheng Wang, Xianghua Zhang, Chunming Gao, Yuxia Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

ISBN: (纸本)9781728176871

A new algorithm of fault diagnosis for injection molding machine based on artificial immune system is proposed. Firstly, the sample data collected from the injection molding machine in different working conditions is analyzed, and the eigenvalues such as mean and variance are obtained and converted into binary strings. These binary strings are called as self-strings. Based on the self-strings, the negative selection algorithm is combined with the clonal selection algorithm to generate a fault detector, which is lased for fault detection;and the positive selection algorithm and the clonal selection algorithm are combined to produce a series of separators, which are then used for fault isolation. Finally, simulations are conducted to verify the effectiveness of the proposed method.

关键词： Artificial immune system Negative/Positive selection algorithm clonal selection algorithm Injection molding machine

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Elastic and Inelastic Content Distribution based on clonal selection in VANETs

Elastic and Inelastic Content Distribution based on Clonal S...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Hu, Jinna Chen, Chen Qiu, Tie Atiquzzaman, Mohammed Pei, Qingqi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Univ Oklahoma Sch Comp Sci Norman OK 73019 USA Shaanxi Key Lab Blockchain & Secure Comp Xian 710071 Peoples R China

ISBN: (纸本)9781728109626

In Vehicular Ad Hoc Networks (VANETs), the unreliable wireless environment and highly dynamic topology make multi-content distribution inefficient due to the large amount of content requests from vehicles. This gives rise to the need for new content distribution schemes in VANETs. Current researches on content distribution for VANETs generally focus on the single type of content. In this paper, we consider both elastic (with no hard delay requirement) and inelastic (with a hard deadline) contents and propose a joint content distribution scheme in VANETs. We model the content distribution as an optimization problem which jointly minimizes the waiting delay of elastic requests and the failure ratio of inelastic requests. Then, we propose an efficient clonal selection-based approximate algorithm to solve the optimization problem. The performance of our scheme is evaluated by simulation using realistic vehicular traces. Simulation results show that our proposed scheme has better performance than previous solutions.

关键词： VANETs content distribution inelastic contents clonal selection algorithm

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An efficient hybrid meta-heuristic approach for cell formation problem

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SOFT COMPUTING 2019年第19期23卷 9189-9213页

作者： Nalluri, Madhu Sudana Rao Kannan, K. Gao, Xiao-Zhi Roy, Diptendu Sinha SASTRA Deemed Univ Sch Comp Thanjavur Tamil Nadu India SASTRA Deemed Univ Sch Humanities & Sci Thanjavur Tamil Nadu India Univ Eastern Finland Sch Comp Kuopio Finland Natl Inst Technol Meghalaya Dept Comp Sci & Engn Shillong Meghalaya India

The cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) has been presented and computational results have been obtained for the proposed scheme with a set of 52 benchmark instances collected from literature. The results presented herein demonstrate that overall proposed HCSA is much more promising in comparison with existing approaches available in recent literatures. Extensive statistical and convergence tests have been carried out to ratify the superiority of the proposed HCSA. The improvements can be attributed to the collaborative interactions in the CSA mechanism, the proposed hybridization for initial population generation and so forth.

关键词： clonal selection algorithm Cell formation problem Hyper-mutation Grouping Genetic algorithms

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Stress-enhanced clonal selection algorithm for structural topology optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2012年第8期89卷 957-974页

作者： Wu, C. Y. Ku, C. C. Tatung Univ Dept Mech Engn Taipei Taiwan

Recently, numerous modified versions of immune algorithms (IAs) have been adopted in both theoretical and practical applications. However, few have been proposed for solving structural topology optimization problems. In addition, the design connectivity handling and one-node connected hinge prevention, which are vital in the application of population-based methods with binary representation for structural topology optimization, have not been applied to IAs in the literature. A stress-enhanced clonal selection algorithm (SECSA) incorporating an IA with a dominance-based constraint-handling technique and a new stress-enhanced hypermutation operator is proposed to rectify those deficiencies. To demonstrate the high viability of the presented method, comparisons between the presented SECSA and genetic algorithm-based methods were made on minimum compliance and minimum weight benchmark structural topology design problems in two-dimensional, three-dimensional, and multiloading cases. In each case, SECSA was shown to be competitive in terms of convergence speed and solution quality. The main goal of this study is not only to further explore the capabilities of IAs, but also to show that an IA with appropriate enhancements can lead to the development of attractive computational tools for global search in structural topology optimization. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： topology optimization clonal selection algorithm stress-enhanced hypermutation operator

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Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

Results in Earth Sciences

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Results in Earth Sciences 2024年 2卷

作者： Nasser Keshavarz Faraj Khah Bahare Salehi Pooria Kianoush Shahab Varkouhi Geoscience Faculty Research Institute of Petroleum Industry (RIPI) Tehran Iran Brussels Polytechnic School Université Libre de Bruxelles (ULB) Brussels Belgium Department of Petroleum and Mining Engineering South Tehran Branch Islamic Azad University Tehran Iran National Iranian Oil Company Exploration Directorate (NIOC-EXP) Tehran Iran Department of Earth Sciences University College London London WC1E 6BT UK

The hydrate concentration model considerably affects elastic properties, including bulk and shear modulus. Defining seismic properties of sediments, such as compressional and shear wave velocity and density, provides valuable information to identify rock facies and fluid types. This information commonly results from pre-stack seismic inversion, while post-stack seismic information provides acoustic impedance as a layer-based property. Traditionally, seismic inversion requires well logs to produce an initial guess of inversion routines and provide a low-frequency part of the amplitude spectrum. Eventually, seismic inversion methods could not be performed in the areas without well-log data, such as deep sea areas. In such cases, pseudo-well logs derived from pre-stack seismic data are a solution. Pseudo-well generation is a title used to estimate the elastic parameters of sediments in areas, such as deep marine environments, where drilled wells are absent or sparse. Metaheuristic optimization algorithms are suitable tools for minimizing the cost function as they best match real and synthetic seismic data. In this study, the SEAM earth model has been used as a reference to investigate the quality of pseudo-well generation utilizing a simulated annealing (SA) algorithm as an optimization method of property model change, which minimizes the cost function of seismic inversion. As a result, considering an initial model type of the SEAM model, simultaneous seismic inversion introduced the best compressional and shear wave velocities and density logs, which provide the best real and synthetic seismic data match when synthetic data is calculated from the simplified Zoeppritz equation.

关键词： ρ Rho Density Kg/m 3 AI Acoustic impedance [m/s*g/cm 3 ] ANNs Artificial neural networks AVA Amplitude variation with angle AVO amplitude variation with offset CDP Common depth-point CMP Common midpoint CNN Convolutional Neural Network CO Common offset CSA clonal selection algorithm EAs Evolutionary algorithms FSA Fast simulated annealing GLI Global laboratory initiative GOM Gulf of Mexico model PSTM Pre-stack time migration RC Reflection coefficient RNNs Recurrent neural networks RTM Reverse-time migration SA Simulated Annealing SEAM SEG Advanced Modeling SI Swarm intelligence Vs and Vp Shear and compressional velocities m/s Pseudo wells Seismic inversion Simulated annealing algorithm Metaheuristic algorithms Seismic properties SEAM model

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