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Hybrid artificial immune algorithm for energy-efficient distributed flexible job shop in semiconductor manufacturing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3075-3098页

作者： Chen, Xiaolong Du, Yu Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China HengXing Univ Sch Informat Engn Qingdao Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

In semiconductor manufacturing, constraints, including multiple scheduling routes, complex production processes, and robotic transportation, particularly bring difficult challenges to production schedules. To address this issue, we investigated an energy-efficient distributed flexible job shop scheduling problem with robotic transportation. In the considered problem, two objectives, i.e., the maximum completion time and total energy consumption, are minimized simultaneously. First, two mixed integer linear programming models are constructed, including the sequence-based and position-based models. Then, a hybrid algorithm combining the artificial immune algorithm and variable neighborhood search algorithm is proposed. To generate a population with better performance and diversity, five initialization strategies and an active decoding method are designed. In addition, a novel clone mechanism and an improved suppression process are being developed to improve the exploration abilities. Furthermore, two distinct neighborhood structures and two object-oriented local search heuristics are designed to balance exploration and exploitation capabilities. Eventually, the proposed algorithm achieved better performance with the comparison of three other state-of-the-art algorithms with different scale instances.

关键词： Distributed flexible job shop scheduling problem Robotic transportation Mixed integer linear programming models artificial immune algorithm Variable neighborhood search algorithm

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Feature selection optimized by the artificial immune algorithm based on genome shuffling and conditional lethal mutation

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APPLIED INTELLIGENCE 2023年第11期53卷 13972-13992页

作者： Zhu, Yongbin Li, Tao Lan, Xiaolong Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Improving classification performance is an essential goal for various practical applications. Feature selection has become an important data preprocessing step in machine learning systems. However, many effective methods based on heuristic search strategies have the problem of high running costs. This paper proposes an efficient multiobjective feature selection method based on artificial immune algorithm optimization. It introduces a clone selection algorithm to explore the search space of optimal feature subsets. According to the target requirements of feature selection, combined with biological research results, this method introduces genome shuffling technology and a conditional lethal mutation mechanism to improve the search performance of the algorithm. Experimental comparisons are conducted on 21 benchmark datasets with 17 advanced feature selection methods in terms of classification accuracy, the number of feature subsets, and computational cost. The results show that the algorithm achieves the smallest number of selected features (only 3.26% compared to the lowest) and better average classification accuracy with a much lower average computational cost than others (only 3.62% compared to the lowest).

关键词： Feature selection Dimensionality reduction Multiobjective optimization artificial immune algorithm Classification

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Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

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Transportation Safety and Environment 2023年第4期5卷 117-126页

作者： Xiaochun Wu Weikang Yang Jianrong Cao School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070GansuChina China Railway Rolling Stock Corporation Qingdao Sifang Rolling Stock Research Institute Co. LtdQingdao 266000ShandongChina

Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is *** the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate *** results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power *** in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault ***,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis *** substantiates the validity and feasibility of the presented approach.

关键词： railway point machines wavelet threshold denoising Mallat wavelet decomposition artificial immune algorithm

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Evaluation and improvement of the creativity of works of art by integrating artificial immune algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Xiuming Xinxiang Univ Acad Fine Arts Xinxiang 453003 Peoples R China

The creativity of artistic works encompasses multifaceted elements such as color, shape, and texture, characterized by their complexity and subtlety. Current evaluation methods are predominantly subjective, lacking the ability to objectively and comprehensively capture and quantify these dynamic attributes. To address this limitation, this study proposes an innovative approach for evaluating artistic creativity by integrating an artificial immune algorithm (AIA). Initially, features related to color, shape, and texture are extracted from the artworks, and these feature vectors are input into the AIA as antigens. Subsequently, by defining antigen-antibody matching rules, the features of the artworks are compared with creative reference antibodies generated by the algorithm to derive creativity evaluation results. Finally, leveraging a dynamic adjustment mechanism for bidirectional crossover mutation probabilities, the diversity of the antibody population is optimized through a clonal selection strategy, enhancing the model's adaptability to diverse artistic styles. Experimental results demonstrate that the proposed improved AIA achieves significant enhancements in evaluation metrics, with average Precision and Recall values increasing by 8.45% and 11.21%, respectively, compared to the baseline AIA. This study concludes that the AIA-integrated creativity evaluation method effectively improves the objectivity and accuracy of artistic assessments, offering a novel perspective for the intelligent evaluation of artworks.

关键词： Creative evaluation artificial immune algorithm artistic creation clonal selection mechanism feedback and iteration

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Trajectory Planning for UAV Based on Improved artificial immune algorithm 2

Trajectory Planning for UAV Based on Improved Artificial Imm...

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2nd International Conference on algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Ye, Zi Yi, Zihao Li, Yuxuan School of Information Engineering Wuhan University of Technology Wuhan China School of Science Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350390254

Our study centers on the multi-UAV multi-store delivery problem and considers the power problem of the UAV for the charging setup of the UAV. In our study, we developed an optimized mathematical model for the problem background and setting, and we selected an artificial immune algorithm to solve the problem with reference to existing studies. In the solution, we proposed equal-length encoding and IPOX crossover operation for the encoding problem of the solution vector, and based on the traditional algorithm, we proposed a two-population strategy to improve the traditional algorithm in order to improve the information interaction of the populations and to jump out of the local optimum, and we introduced an adaptive variation probability to enhance the convergence of the algorithm, and finally, we compared the improved algorithm with the traditional algorithm and came up with the results of the effectiveness and the improvement of the traditional algorithm in solving the problem. We finally compare the improved algorithm with the traditional algorithm and conclude the effectiveness of our improvement on the problem and its superiority over the traditional algorithm. © 2024 IEEE.

关键词： adaptive variation probability artificial immune algorithm IPOX crossover two-population strategy

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The Analysis of Blockchain Digital Currency Product Innovation Based on artificial immune algorithm

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IEEE ACCESS 2022年 10卷 132448-132454页

作者： Chen, Hong Su, Kecheng Gao, Wandong Guangzhou Xinhua Univ Sch Management Guangzhou 510520 Peoples R China Univ Southern Calif Marshall Business Sch Los Angeles CA 90007 USA Weifang Univ Sci & Technol Sch Econ & Management Shouguang 262700 Peoples R China

In recent years, with the rapid growth of the digital currency industry, blockchain technology has attracted wide attention, and its research mostly focuses on the protection of economic security and privacy. In order to study the application and development mechanism of currency products, this work investigates the practical application of digital currency products under blockchain technology at the present stage, and analyzes the potential security risks. The artificial immune algorithm (AIA) is an intelligent method to imitate the function of the biological immune system, which provides a new solution for solving complex distributed problems. Firstly, a blockchain digital currency product is proposed based on AIA. Secondly, taking the credit risk of innovation risk in financial currency products as an example, the Diffie-Hellman (DH) with blockchain technology is used. Finally, the generation and optimization of the plan are realized, and an innovative risk plan is proposed for blockchain digital currency products on the basis of AIA. The results show that: (1) The improved AIA implements a guided AIA with adaptive parameter update. (2) The digital signature technology using the DH algorithm has a stronger anti-attack ability than the traditional blockchain digital signature technology. It improves the security of blockchain digital currency. This work has successfully established the risk-immune system of digital currency product innovation, laying a theoretical foundation for the innovation and development of blockchain digital currency products in the future.

关键词： artificial intelligence artificial immune algorithm blockchain DH algorithm digital currency innovation risk potential risk

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Path Tracking of a Two-Wheeled Self-Balancing Robot Based on Multi-objective Optimization with artificial immune algorithm 7

Path Tracking of a Two-Wheeled Self-Balancing Robot Based on...

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7th International Conference on Robotics and Automation Sciences (ICRAS)

作者： Wang, Jiayuan Wuhan Univ Technol Sch Int Educ Wuhan Peoples R China

ISBN: (纸本)9798350313567

In this paper, a multi-objective optimization-based path tracking method for a two-wheeled self-balancing robot (TWSBR) is proposed. First, the dynamics model of the system is established based on the Newton-Euler method and simulated in simulink. Then an artificial immune algorithm is used to find the optimal motion parameters for each control point based on the parameter feedback of the TWSBR, and finally a proportional-integral-derivative controller (PID) is used for control. The advantages of this study are as follows: (1) The stability of the TWSBR in high-speed working condition is improved and the efficiency of the robot is enhanced by the optimization algorithm. (2) The anti-jamming capability of the TWSBR is improved through parameter feedback. (3) The optimized PID controller is used to control the motor torque, which avoids manual adjustment of parameters and improves the control efficiency. The method can be applied to TWSBR with fixed paths such as logistics distribution and space disinfection.

关键词： artificial immune algorithm two-wheeled self-balancing robot path tracking

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Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified artificial immune algorithm

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IEEE ACCESS 2021年 9卷 75683-75698页

作者： Lu, Liping Guo, Zhe Wang, Zhiqi Huang, Zhonghua Liu, Ming Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

关键词： Integrated circuit modeling Couplings Metals Mathematical model Equivalent circuits Data models Computational modeling Channel model optimization parameter estimation artificial immune algorithm capacitive coupling sensor networks

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Reactive power optimization based on adaptive multi-objective optimization artificial immune algorithm

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AIN SHAMS ENGINEERING JOURNAL 2022年第5期13卷

作者： Lian, Lian Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Peoples R China Shenyang Econ & Technol Dev Zone Coll Informat Engn Room 30311th St Shenyang 110142 Peoples R China

In this study, an adaptive multi-objective optimization artificial immune algorithm is presented for reactive power optimization. In the proposed algorithm, a non-inferior solution ranking method based on Pareto coefficient is proposed to rank antibodies. The fitness evaluation mechanism based on individual neighborhood selection and adaptive cloning operator ensure the convergence of the algorithm, and the chaotic random sequence is added to the mutation operator to improve the diversity of the antibody population. Considering the minimum active power loss, the maximum static voltage stability margin and the best voltage level, a multi-objective reactive power optimization model is established by introducing the static voltage stability index. IEEE-30 bus system is chosen as a research object. Combined with technique for order preference by similarity to ideal solution method, after the multi-attribute decision making of the Pareto solution set, the optimal solution cannot only ensure the economic operation of the system, but also enhance the voltage stability of the power grid. The designed reactive power optimization algorithm is effective.@2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： Reactive power optimization artificial immune algorithm Multi-objective optimization Pareto sort Chaotic mutation

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Two-level Optimization Model of Construction Safety Control and artificial immune algorithm

Two-level Optimization Model of Construction Safety Control ...

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第35届中国控制与决策会议

作者： Chunguang Chang Zhuo Zuo Lianjie Yang School of Management Shenyang Jianzhu University

To improve the construction safety degree,a twolevel construction safety control optimization model system is established for the complex construction safety control *** the first-level optimization model,the maximum cumulative construction safety improvement rate brought by the input of the major resources of the project is taken as the objective *** the constraints,the restriction of the sum of the input of each major resource and the proportion of the input of each major resource are *** main input values obtained by the first level optimization model are taken as an important prerequisite for the second-level optimization *** the second-level optimization model system,the construction safety control optimization models of three resources are established:construction worker allocation,construction material use and construction tower crane allocation *** immune algorithm(AIA) is adopted to solve above two-level *** representing for above varied models on two levels and the detail steps of AIA is are *** scientificity and effectiveness of above twooptimization model system and AIA are verified.

关键词： two-level model building construction safety control optimization artificial immune algorithm

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