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Improved magnet system by multi-objective particle swarm optimization for Faraday rotators

JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2025年 624卷

作者： Qiao, Rui Hu, Zhaohui Sun, Jinji Beihang Univ Inst Large Scale Sci Facil Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Natl Inst Extremely Weak Magnet Field Infrastruct Hangzhou 310051 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Hefei Natl Lab Hefei 230088 Peoples R China

The capability of optical isolation relies heavily on the performance of Faraday Rotator. Especially for miniaturized applications, volume efficient optical isolation is becoming a challenge and has raised widespread attention. The difficulty is enhancing the strength and uniformity of the magnetic field within limited volume. In this study, a novel magnet system that applies to Faraday Rotators is proposed. Compared with traditional approach, our design takes into account both the strength and uniformity of the magnetic field with multi-objective optimization through finite element analysis. An improved model was built based on the traditional Halbach Array for the prove of concept. Results show that, the improved magnet system has achieved a slight improvement in terms of magnetic field strength and has enhanced its uniformity by more than 20%. This method can be applied to various applications by adjusting the constraints during optimization, which pave the way for the further miniaturization of Faraday Rotators.

关键词： Faraday rotator Permanent magnets multi-objective particle swarm optimization

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A multi-objective particle swarm optimization with a competitive hybrid learning strategy

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COMPLEX & INTELLIGENT SYSTEMS 2024年第4期10卷 5625-5651页

作者： Chen, Fei Liu, Yanmin Yang, Jie Liu, Jun Zhang, Xianzi Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China Zunyi Normal Coll Zunyi 563002 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

To counterbalance the abilities of global exploration and local exploitation of algorithm and enhance its comprehensive performance, a multi-objective particle swarm optimization with a competitive hybrid learning strategy (CHLMOPSO) is put forward. With regards to this, the paper first puts forward a derivative treatment strategy of personal best to promote the optimization ability of particles. Next, an adaptive flight parameter adjustment strategy is designed in accordance with the evolutionary state of particles to equilibrate the exploitation and exploration abilities of the algorithm. Additionally, a competitive hybrid learning strategy is presented. According to the outcomes of the competition, various particles decide on various updating strategies. Finally, an optimal angle distance strategy is proposed to maintain archive effectively. CHLMOPSO is compared with other algorithms through simulation experiments on 22 benchmark problems. The results demonstrate that CHLMOPSO has satisfactory performance.

关键词： multi-objective particle swarm optimization Derivative treatment strategy Hybrid learning Optimal angle distance

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An Improved multi-objective particle swarm optimization Routing on MANET

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Computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using multi-objective particle swarm optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

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COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 435-462页

作者： Geng, Aifeng Lv, Qing Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China

Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm ((DCMOPSO)-C-2). The sophistication of the (DCMOPSO)-C-2 based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

关键词： Sensor ontology Ontology meta-matching multi-objective particle swarm optimization Competitive mechanism Ontology Alignment Evaluation Initiative

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Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization

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COMPLEX & INTELLIGENT SYSTEMS 2025年第6期11卷 1-25页

作者： Bao, Xiang Han, Fei Ling, Qinghua Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China Jiangsu Engn Res Ctr Big Data Ubiquitous Percept & Zhenjiang 212013 Peoples R China Jiangsu Univ Inst Sci & Technol Informat Zhenjiang 212013 Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Peoples R China

Label correlations, as important prior information, are essential to enhance the classification performance in multi-Instance multi-Label (MIML) algorithms, but existing models always leverage global label correlations which are less informative. Furthermore, classifier optimization is also crucial for MIML classification results, previous works do not frequently seek to optimize multi objectives simultaneously which may easily result in poor performance on some important metrics. In this paper, a MIML algorithm encoded with local label correlations with an improved multi-objective particle swarm optimization (MIML-MOPSO-LLC) is proposed to address the above problems. Specifically, a framework is proposed by taking consideration into both global discrimination fitting and local label correlation sensitivity in the bag level simultaneously in the standard MIML. Subsequently, the loss function of the framework is solved by an alternating optimization process where Support Vector Machine (SVM) classifiers are constructed for optimization. Ultimately, an improved MOPSO is employed to optimize the SVM classifiers by searching for more reliable Pareto front solutions. The experimental results demonstrate that the proposed method achieves competitive performance compared with the classical and state-of-the-art MIML models from the perspective of several classification indicators. Notably, the proposed method which explores the local label correlations exhibits superiority over methods relying on global ones. Furthermore, the study reveals that proposed methods demonstrates enhanced effectiveness in MIML classification and optimizing SVM classifiers compared to conventional single and multi objective optimization approaches.

关键词： multi-instance multi-label Local label correlations multi-objective particle swarm optimization Pareto front

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A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization

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COMPLEX & INTELLIGENT SYSTEMS 2023年第6期9卷 7523-7548页

作者： Liu, Jun Liu, Yanmin Han, Huayao Zhang, Xianzi Shu, Xiaoli Chen, Fei Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China Zunyi Normal Coll Sch Math Zunyi 563002 Peoples R China Guizhou Xingqian Informat Technol Co Ltd Zunyi 563002 Peoples R China Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China

In multi-objective particle swarm optimization, it is very important to select the personal best and the global best. These leaders are expected to effectively guide the population toward the true Pareto front. In this paper, we propose a two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization (TMMOPSO), which adaptively selects the global best and updates the personal best by means of hyper-cone domain and aggregation, respectively. This strategy enhances the global exploration and local exploitation abilities of the population. In addition, the excellent particles are perturbed and a two-stage maintenance strategy is used for the external archive. This strategy not only improves the quality of the solutions in the population but also accelerates the convergence speed of the population. In this paper, the proposed algorithm is compared with several multi-objective optimization algorithms on 29 benchmark problems. The experimental results show that TMMOPSO is effective and outperforms the comparison algorithms on most of the 29 benchmark problems.

关键词： multi-objective particle swarm optimization Hyper-cone domain Two-stage maintenance multi-objective optimization

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multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Zhang, Xiangyin Xia, Shuang Li, Xiuzhi Zhang, Tian Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning problem under complex environments with multiple constraints, the multi-objective particle swarm optimization algorithm with multi-mode collaboration based on reinforcement learning (MCMOPSO-RL) is proposed in this paper to find optimal paths and handle constraints simultaneously. Reinforcement learning (RL) is applied to enable the proposed algorithm to choose the suitable position updated mode to achieve the high performance. multi-mode collaboration strategy is developed to update the particle positions, where three modes are designed to balance the population diversity and the convergence speed, including the exploration, exploitation modes, and the hybrid update mode. Experimental results show that MCMOPSO-RL can solve the path planning problem for multiple UAVs more efficiently and robustly than other algorithms. (C) 2022 Published by Elsevier B.V.

关键词： Path planning multi-objective particle swarm optimization Unmanned air vehicle Reinforcement learning

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Adaptive candidate estimation-assisted multi-objective particle swarm optimization

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Science China(Technological Sciences) 2022年第8期65卷 1685-1699页

作者： HAN HongGui ZHANG LinLin HOU Ying QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity.

关键词： multi-objective particle swarm optimization evolutionary state information adaptive candidate estimation convergence and diversity convergence analysis

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Milling optimization of GH4169 nickel-based superalloy under minimal quantity lubrication condition based on multi-objective particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期123卷 3983-3994页

作者： Guo, Chenguang Chen, Xiaodong Li, Qiang Ding, Guangshuo Yue, Haitao Zhang, Jianzhuo Liaoning Tech Univ Sch Mech Engn Fuxing 123000 Peoples R China

The surface quality of machining will greatly affect the service life of components. In this paper, to reduce the surface roughness and cutting force of nickel-based superalloy during machining, the milling parameters of GH4169 nickel-base superalloy under micro-lubrication conditions were optimized. Based on the response surface method, the influence of milling parameters (spindle speed, feed per tooth, micro-lubricating oil, and micro-lubricating pressure) on the surface roughness and cutting force was analysed;through the analysis of variance, the significant interaction effect and the regularity between each index were obtained, and the optimization models of surface roughness, redial, and axial cutting force were established;the multi-objective particle swarm optimization algorithm was used to optimize the model based on crowding distance sorting;the optimal combination of process parameters was determined to reduce surface roughness and achieve less cutting force. The research results show that the feed per tooth and the air pressure have the most obvious influence on the surface roughness;the interaction effect of the feed per tooth and the oil volume has the most significant influence on the radial and axial cutting forces. The radial and axial cutting forces show a trend of increasing with the increase of the feed per tooth, and decreasing with the increase of the oil amount;the optimal parameter combination was used to carry out the machining experiment, and the surface roughness and diameter. The relative error between the measured value of tangential cutting force and the predicted value of the model is 8.14%, the surface of the specimen is good, and the lowest surface roughness and cutting force are obtained, which has certain guiding significance for the successful research on the side milling of GH4169 nickel-based superalloy.

关键词： multi-objective particle swarm optimization Minimal quantity lubrication optimization model Surface roughness Cutting force

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Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter optimization, Machine Learning Techniques, and multi-objective particle swarm optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-22页

作者： Chintakindi, Sanjay Alsamhan, Ali Abidi, Mustufa Haider Kumar, Maduri Praveen King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Adv Mfg Inst Riyadh 11421 Saudi Arabia Univ Leicester Leicester Leics England

The purpose of this paper is to investigate the effect of the annealing process at 1000 degrees C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (psi(1) = 64.5), tool wear (psi(2) = 22.3) and metal removal rate (psi(3) = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows;surface roughness (2.5 mu m, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm(3)/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

关键词： Monel-400 Annealing Principal component analysis Optuna Machine learning multi-objective particle swarm optimization

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