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22nd International Conference on Electrical Machines and Systems (ICEMS)

作者： Zhang, Wenyi Li, Xiaolong Qiao, Jiaqi Liu, Xiaoyan Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)9781728133980

In the frequency conversion device, the DC voltage utilization rate is one of the important indicators to measure the advantages and disadvantages of the modulation method, and also determines the output capability of the inverter. In the three-phase bridge inverter, the DC voltage utilization rate of the conventional SPWM control method is only 0.866, and the DC voltage utilization ratio of the relatively superior SVPWM control method can reach 1. In this thesis, a three-phase bridge inverter is taken as the research object. Based on the immune algorithm, the affinity evaluation operator and the incentive calculation operator are designed for the mathematical model of the three-phase bridge inverter. Under the condition, the SHEPWM nonlinear transcendental equations are solved, and a reasonable switching angle is calculated, and then the simulation experiment is carried out. The simulation results show that the SHEPWM control method has the characteristics of high DC voltage utilization and small voltage and current harmonic content compared with SPWM control method and SVPWM control method. On the other hand, according to the simulation data, the maximum value of the DC voltage utilization of the SHEPWM control method can be obtained between 1.022 and 1.031.

关键词： immune algorithm three-phase bridge inverter SHEPWM SVPWM DC voltage utilization

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An immune algorithm that Uses a Master Cell for Component Selection for the kNN Method

An Immune Algorithm that Uses a Master Cell for Component Se...

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Conference on Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE)

作者： Palacios, A. P. Suzuki, Y. Toin Univ Yokohama Fac Biomed Engn Yokohama Kanagawa Japan Toin Univ Yokohama Grad Sch Engn Yokohama Kanagawa Japan

ISBN: (纸本)9781728100371

This paper details an immune algorithm (IA) that improves the accuracy of a kNN (k-Nearest Neighbor) method when using it for prognosis and diagnosis. Our IA uses a master cell to produce new groups of immune candidate cells. The IA selects the best components that must be used by the kNN method. The best combinations of components found by our IA helped to improve the average accuracy of the kNN method by 1% for the UCI breast cancer data (prognosis) and almost 3% for the UCI Cleveland heart (diagnosis) data set.

关键词： breast cancer component selection diagnosis heart disease immune algorithm kNN master cell prognosis

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Congestion Control in ATM networks using PID Controller with immune algorithm 10

Congestion Control in ATM networks using PID Controller with...

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10th International Conference on Information and Communication Systems (ICICS)

作者： Shatnawi, Yousef Quwaider, Muhannad Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan Jordan Univ Sci & Technol Dept Comp Engn Irbid Jordan

ISBN: (纸本)9781728100456

In this paper, the Proportional-Integrator-Differentiator (PID) controller is tuned in a rate-based congestion control scheme to be used for the ATM networks. The tuning strategy is based on reformulating the problem to be as an optimization problem with a compact fitness function reflecting the control requirements. The key contribution in this paper is to adopt the immune algorithm as the tuning process, which proves to be so simple, fast, and computationally cheap compared with the other traditional tuning methods. For the purpose of comparison, a simplified network model is built and simulated using two control methods;namely: the proposed control scheme and the well-known single-bit indicator scheme. The experimental results prove that the PID controller tuned by the immune algorithm is superior in terms of the stability of both, buffer occupancy in the switch and the source rate. Both the transient behavior and the steady-state response for the PID controller are shown to be much better than the single-bit indicator scheme. Consequently, the proposed control scheme outperforms the single-bit indicator in terms of link utilization, buffer utilization, packet drop and robustness against any burst change in the network environment.

关键词： ATM network congestion control PID controller immune algorithm

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Essential Protein Detection from Protein-Protein Interaction Networks Using immune algorithm 7th

Essential Protein Detection from Protein-Protein Interaction...

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7th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Yang, Xiaoqin Lei, Xiujuan Wang, Jiayin Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9783030179380;9783030179373

The prediction of essential proteins in protein-protein interaction (PPI) networks plays a pivotal part in improving the cognition of biological organisms. This study presents a novel computational technique, called EPIA, to discover essential proteins by employing immune algorithm. In EPIA, each antibody denotes a candidate essential protein set, which is initialized in a random way among all proteins in a PPI network. Then the vaccine is extracted based on the prediction results of the existing essential protein identification methods. Next, EPIA utilizes four operators, crossover, mutation, vaccination and immune selection to update the antibody population and search for the optimal candidate essential protein set. The experimental results on two species (Saccharomyces cerevisiae and Drosophila melanogaster) demonstrate that EPIA can obtain a better performance on identifying essential proteins compared to other existing methods.

关键词： Essential protein detection immune algorithm PPI network GO annotation

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Feature Selection Using an Improved Multi-objective immune algorithm for Intrusion Detection

Feature Selection Using an Improved Multi-objective Immune A...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Wei, Wenhong Chen, Shuo Lin, Qiuzhen Ji, Junkai Chen, Jianyong Dongguan Univ Technol Sch Comp Sci Dongguan Peoples R China Shenzhen Univ Shenzhen Sch Comp & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728124858

Feature selection is important for running data classification. By selecting a suitable subset of features, the training time of the classification model can be reduced while the classification accuracy can be improved. In this paper, we propose a feature selection method using an improved multi -objective immune algorithm for intrusion detection. We modified a traditional multi-objective immune algorithm using an elite selection strategy based on the reference vectors, which not only can solve the imbalanced classification problem, but also can have a faster convergence speed. Experimental results on the NSL-KDD dataset show the higher classification accuracy of the proposed algorithm.

关键词： feature selection immune algorithm multi-objective optimization intrusion detection

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A survey for recent applications and variants of nature-inspired immune search algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2020年第4期63卷 354-370页

作者： Alkhateeb, Faisal Al-Khatib, Ra'ed M. Doush, Iyad Abu Yarmouk Univ Comp Sci Dept Irbid Jordan Amer Univ Kuwait Comp Dept Salmiya Kuwait

Artificial immune Systems (AIS) is a well-known nature inspired and population based algorithm that proved its effectiveness for solving engineering and practical real-world problems. AIS can adapt to learning, has many models for different immune systems, which can be used to tackle different kinds of optimisation problems, and it can also be hybridised with other algorithms. In this paper, we extensively summarise the recent researches of AIS and categorise them based on the application problem to understand the current trend of the usage of this algorithm. In addition, we provide the up to date open research problems that are not solved by immune search algorithm, and they were solved recently by other algorithms. This can help in paving the road for future research directions in the AIS field.

关键词： artificial intelligence immune algorithm nature-inspired metaheuristics optimisations applications variants of immune algorithm

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A Resource Allocation Scheme Based on immune algorithm for D2D-Based Vehicular Communication Networks

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IEEE ACCESS 2019年 7卷 122536-122543页

作者： Li, Xujie Sun, Ying Zhou, Lingjie Qi, Ailin Zhou, Siyuan Hohai Univ Coll Comp & Informat Nanjing 211100 Jiangsu Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China

For D2D-based vehicular communication networks, how to effectively and quickly allocate frequency resources is a big challenge. In this paper, a resource allocation scheme based on immune algorithm for D2D-based vehicular communication networks is proposed. Firstly, the D2D-based vehicular communication system model is described. Then the resource allocation issue in D2D-based vehicular communication networks is formulated and analyzed. Next, an efficient resource allocation scheme based on immune algorithm is presented. Finally, the analysis and simulation results validate the high efficiency of the proposed scheme compared with other algorithms. This result can provide the guidance for the deployment of VUE communication system.

关键词： Resource allocation immune algorithm vehicular communication networks

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A novel multi-objective immune algorithm with a decomposition-based clonal selection

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APPLIED SOFT COMPUTING 2019年 81卷 105490-000页

作者： Li, Lingjie Lin, Qiuzhen Liu, Songbai Gong, Dunwei Coello Coello, Carlos A. Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China IPN CINVESTAV Dept Comp Sci Mexico City 07360 DF Mexico

In recent years, a number of multi-objective immune algorithms (MOIAs) have been proposed as inspired by the information processing in biologic immune system. Since most MOIAs encourage to search around some boundary and less-crowded areas using the clonal selection principle, they have been validated to show the effectiveness on tackling various kinds of multi-objective optimization problems (MOPs). The crowding distance metric is often used in MOIAs as a diversity metric to reflect the status of population's diversity, which is employed to clone less-crowded individuals for evolution. However, this kind of cloning may encounter some difficulties when tackling some complicated MOPs (e.g., the OF problems with variable linkages). To alleviate the above difficulties, a novel MOIA with a decomposition-based clonal selection strategy (MOIA-DCSS) is proposed in this paper. Each individual is associated to one subproblem using the decomposition approach and then the performance enhancement on each subproblem can be easily quantified. Then, a novel decomposition-based clonal selection strategy is designed to clone the solutions with the larger improvements for the subproblems, which encourages to search around these subproblems. Moreover, differential evolution is employed in MOIA-DCSS to strength the exploration ability and also to improve the population's diversity. To evaluate the performance of MOIA-DCSS, twenty-eight test problems are used with the complicated Pareto-optimal sets and fronts. The experimental results validate the superiority of MOIA-DCSS over four state-of-the-art multi-objective algorithms (i.e., NSLS, MOEA/D-M2M, MOEA/D-DRA and MOEA/DD) and three competitive MOIAs (i.e., NNIA, HEIA, and AIMA). (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization immune algorithm Differential evolution Clonal selection

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A multi-objective immune algorithm with dynamic population strategy

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100477-000页

作者： Lin, Qiuzhen Zhu, Qingling Wang, Na Huang, Peizhi Wang, Wenjun Chen, Jianyong Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In this paper, we propose a multi-objective immune algorithm with dynamic population strategy, named MOIA-DPS, which introduces a control strategy of dynamic population size into multi-objective immune algorithm (MOIA). This scheme helps to compensate the lack of diversity due to the clonal principle in MOIA and adequately exploits the computational resource during the evolutionary progress. In MOIA-DPS, the status of external archive (full or not full) is used to decide the enlargement or the reduction of population size, so as to adaptively adjust the computational resource. Moreover, in order to further enhance the robustness of MOIA-DPS, we present an effective DE operator with two search models, called TDE, in which two search models such as rand/2/bin and rand/1/bin are alternatively exchanged according to a probability. When compared to four state-of-the-art heuristic algorithms, i.e., I-SDE +, MOEA/D-GRA, AbYSS, CMPSO, and four MOIAs, i.e., IMADE, DMMO, HEIA, and ALMA, MOIA-DPS was shown to present several advantages in solving different sets of benchmark problems.

关键词： Multi-objective optimization immune algorithm Dynamic population strategy Differential evolution

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Research on Active Disturbance Rejection Control Method for Bidirectional DC-DC Converter Based on immune algorithm

Research on Active Disturbance Rejection Control Method for ...

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IEEE International Power Electronics and Application Conference and Exposition (IEEE PEAC)

作者： Yang Hui Luo Shan Sun Xiangdong Chao Kaiyue Xian Univ Technol Automat & Informat Engn Inst Xian Shaanxi Peoples R China

ISBN: (纸本)9781538660546

In a specific multi-source disturbance system such as photovoltaic energy storage, the photovoltaic output power change and load mutation may have a great impact on the DC bus voltage, then affect the system stable operation. In order to restrain the DC bus voltage fluctuation, a novel active disturbance rejection control method for bidirectional DC-DC converter based on immune algorithm is proposed in this paper. The immune algorithm is studied on the basis of the immune response system, and then the algorithm realization is analyzed. The immune algorithm is introduced into the active disturbance rejection controller, and the parameters of the expansion state observer and nonlinear feedback control law all can be adjusted online by using the immune algorithm, and the dynamic performance of the system under the condition of multi-source disturbances has been further improved. The experimental results verify the effectiveness of the active disturbance rejection control method for bidirectional DC-DC converter based on immune algorithm.

关键词： photovoltaic energy storage system bidirectional DC-DC converter Active disturbance rejection control immune algorithm

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