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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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Fixed-Time and Predefined-Time Synchronization of Inertial Memristive Neural Networks With Proportional Delay and Mismatched Switching Jumps via Stochastic Particle Swarm Optimization 认领 引用
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作者 Qiang Lai Jun Wang +1 位作者 Leimin Wang Minghong Qin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1274-1287,共14页
This paper studies the fixed-time synchronization(FxTS)and predefined-time synchronization(PTS)of a class of inertial memristive neural networks(IMNNs),which have unbounded proportional delay independent of time linea... This paper studies the fixed-time synchronization(FxTS)and predefined-time synchronization(PTS)of a class of inertial memristive neural networks(IMNNs),which have unbounded proportional delay independent of time linearity and mismatched switching jump coefficients.Based on Filippov solution theory and Lyapunov methods,this work develops an enhanced FxTS criterion delivering a tighter upper bound on convergence time and thereby extending guaranteed fixed-time behavior to a wider range of networks.A refined PTS condition that incorporates additional state-dependent terms accelerates error decay and reduces conservatism during the transient response.Numerical simulations show that the convergence rate of PTS under this strategy is significantly improved.Moreover,an optimization model with minimum control energy and dynamic error as objective functions is proposed to obtain more accurate controller parameters,and the stochastic inertia weight particle swarm optimization(SIWPSO)algorithm is introduced to solve the optimization model.Numerical studies not only validate the theoretical results for both FxTS and PTS but also demonstrate a secure communication application in which the chaos of the IMNNs acts as a masking carrier and enables perfect encryption and decryption of a complex test signal through SIWPSO-optimized FxTS and PTS. 展开更多
关键词 Fixed-time synchronization predefined-time synchronization inertial memristive neural networks proportional delay mismatched switching jumps stochastic particle swarm optimization
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal... Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics. 展开更多
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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PID Steering Control Method of Agricultural Robot Based on Fusion of Particle Swarm Optimization and Genetic Algorithm 认领 引用 被引量：2
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作者 ZHAO Longlian ZHANG Jiachuang +2 位作者 LI Mei DONG Zhicheng LI Junhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期358-367,共10页
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion... Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots. 展开更多
关键词 agricultural robot steering PID control particle swarm optimization algorithm genetic algorithm
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A hybrid method based on particle swarm optimization and machine learning algorithm for predicting droplet diameter in a microfluidic T-junction 认领 引用
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作者 F.ESLAMI R.KAMALI 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第1期203-214,共12页
Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment... Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences. 展开更多
关键词 droplet-based microfluidics decision tree(DT) particle swarm optimization(PSO) double T-junction grid search(GS)
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Leveraging Opposition-Based Learning in Particle Swarm Optimization for Effective Feature Selection 认领 引用
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作者 Fei Yu Zhenya Diao +3 位作者 Hongrun Wu Yingpin Chen Xuewen Xia Yuanxiang Li 《Computers, Materials & Continua》 SCIE EI 2026年第4期1148-1179,共32页
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par... Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks. 展开更多
关键词 Feature selection fitness landscape opposition-based learning principle of the lever particle swarm optimization
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Structural optimization of magnetic flux concentrator based on particle swarm optimization algorithm 认领 引用
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作者 Yu-Xiao Wang Shi-Yu Guan +3 位作者 Yi Zhang Qi-Yuan Jiang Bing-Feng Sun Zhong-Qi Tan 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期373-383,共11页
This study introduces a structural optimization method for magnetic flux concentrators(MFCs)utilizing the particle swarm optimization(PSO)algorithm,addressing inefficiencies and challenges in achieving optimal structu... This study introduces a structural optimization method for magnetic flux concentrators(MFCs)utilizing the particle swarm optimization(PSO)algorithm,addressing inefficiencies and challenges in achieving optimal structures with traditional methods.By integrating the PSO algorithm with COMSOL Multiphysics simulation software,a co-simulation framework is developed to optimize the parameters of a conical MFC.Compared with traditional methods,this approach enhances optimization results by twofold,achieving a magnetic field amplification factor of up to 200 within the defined parameter range.Additionally,an evaluation criterion for concentrator optimization is proposed,offering novel insights for designing concentrators across various scenarios. 展开更多
关键词 magnetic flux concentrator magnetic sensing enhancement particle swarm optimization
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Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 认领 引用
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作者 Taiyou Chen Xiaomin Hu +1 位作者 Qiuzhen Lin Weineng Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1673-1688,共16页
With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration abi... With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors.In this work,we study distributed multi-target localization problem with measurement-to-measurement association(DM2M),where each sensor only accesses its own measurement data without the association of measurements from other sensors.We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors.Then,we propose a multiagent swarm optimization method with contribution-based cooperation(MASTER).In MASTER,each sensor maintains a particle swarm to represent candidate solutions of target positions.Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively.To address the bilevel local objective function,we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization.To promote sensors to optimize the global objective,we design a contribution-based cooperation method to guide sensors to learn from their neighbors.Through localization experiments for different target numbers and localization dimensions,the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms. 展开更多
关键词 Distributed optimization evolutionary computation measurement-to-measurement association particle swarm optimization(PSO) wireless sensor networks(WSNs)
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Dynamic Weighted Spherical Particle Swarm Optimization for UAV Path Planning in Complex Environments 认领 引用
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作者 Rui Yao Yuye Wang +2 位作者 Fei Yu Hongrun Wu Zhenya Diao 《Computers, Materials & Continua》 SCIE EI 2026年第5期1063-1081,共19页
Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies... Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems. 展开更多
关键词 Dynamic weight adjustment lens opposition learning particle swarm optimization path planning unmanned aerial vehicles
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Particle Swarm Optimization: Advances, Applications, and Experimental Insights 认领 引用 被引量：6
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作者 Laith Abualigah 《Computers, Materials & Continua》 SCIE EI 2025年第2期1539-1592,共54页
Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a... Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future. 展开更多
关键词 Particle swarm optimization(PSO) optimization algorithms data mining machine learning engineer-ing design energy systems healthcare applications robotics comparative analysis algorithm performance evaluation
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Path planning of unmanned surface vehicles based on improved particle swarm optimization algorithm with consideration of particle sight distance 认领 引用
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作者 WANG Cheng YANG Junnan +3 位作者 ZHANG Xinyang QIAN Zhong ZHU Ye LIU Hong 《上海海事大学学报》 北大核心 2026年第1期9-19,共11页
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc... To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs. 展开更多
关键词 particle swarm optimization algorithm(PSO) sight distance unmanned surface vehicle(USV)
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Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection 认领 引用
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作者 Mourad Benmalek Abdessamed Seddiki 《Data Science and Management》 EI CSCD 2025年第4期423-435,共13页
The exponential escalation of cyber threats and attacks targeting Internet of Things(IoT)devices in this decade necessitates the development of effective intrusion detection methods.This paper presents an innovative a... The exponential escalation of cyber threats and attacks targeting Internet of Things(IoT)devices in this decade necessitates the development of effective intrusion detection methods.This paper presents an innovative anomaly-based intrusion detection system that leverages machine learning(ML)and deep learning(DL)models to secure IoT networks.This study utilizes the RT_IoT2022 dataset,a novel dataset that captures complex IoT attack scenarios.The study implements particle swarm optimization(PSO),a bio-inspired metaheuristic,for feature selection and optimization,successfully reducing computational overhead while enhancing model performance.Several models,including the support vector machine,k-nearest neighbors,categorical boosting(CatBoost),naïve Bayes,convolutional neural network,and long short-term memory,have been evaluated for their ability to classify normal and malicious attacks.Our findings underscore the crucial roles of ML and DL in safeguarding IoT networks and the importance of continuous model evaluation using real-world data.Experiments demonstrate that CatBoost combined with PSO outperforms state-of-the-art methods in the literature on the same dataset across all metrics. 展开更多
关键词 Internet of Things Intrusion detection system Particle swarm optimization Machine learning Deep learning Security
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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy 认领 引用 被引量：2
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作者 Li Ma Cai Dai +1 位作者 Xingsi Xue Cheng Peng 《Computers, Materials & Continua》 SCIE EI 2025年第1期997-1026,共30页
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition... The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance. 展开更多
关键词 Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy
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Optimizing Feature Selection by Enhancing Particle Swarm Optimization with Orthogonal Initialization and Crossover Operator 认领 引用 被引量：3
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作者 Indu Bala Wathsala Karunarathne Lewis Mitchell 《Computers, Materials & Continua》 SCIE EI 2025年第7期727-744,共18页
Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Effi... Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Efficient feature selection methods are critical for improving diagnostic accuracy,reducing computational costs,and enhancing the interpretability of predictive models.Particle Swarm Optimization(PSO),a widely used metaheuristic inspired by swarm intelligence,has shown considerable promise in feature selection tasks.However,conventional PSO often suffers from premature convergence and limited exploration capabilities,particularly in high-dimensional spaces.To overcome these limitations,this study proposes an enhanced PSO framework incorporating Orthogonal Initializa-tion and a Crossover Operator(OrPSOC).Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population,substantially improving the algorithm’s exploration capability.The Crossover Operator,inspired by genetic algorithms,introduces additional diversity during the search process,effectively mitigating premature convergence and enhancing global search performance.The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon,Leukemia,and Prostate Tumor.Comparative analyses were conducted against traditional filter-based methods,including Fast Clustering-Based Feature Selection Technique(Fast-C),Minimum Redundancy Maximum Relevance(MinRedMaxRel),and Five-Way Joint Mutual Information(FJMI),as well as prominent metaheuristic algorithms such as standard PSO,Ant Colony Optimization(ACO),Comprehensive Learning Gravitational Search Algorithm(CLGSA),and Fuzzy-Based CLGSA(FCLGSA).Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy,computational efficiency,and result stability,achieving significant improvements even with fewer selected features.Additionally,a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance.These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional,data-driven fields. 展开更多
关键词 Machine learning feature selection classification medical diagnosis orthogonal initialization crossover particle swarm optimization
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Reliability Topology Optimization Based on Kriging-Assisted Level Set Function and Novel Dynamic Hybrid Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 Hang Zhou Xiaojun Ding +1 位作者 Song Chen Qijun Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1907-1933,共27页
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif... Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe. 展开更多
关键词 Reliability topology optimization kriging model level set function dynamic hybrid particle swarm optimization engineering structure
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Estimating Optimal Location of STATCOM and Minimization of Congestion Cost by Locational Marginal Price Using Flower Pollination and Particle Swarm Optimization Techniques 认领 引用 被引量：1
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作者 Gagandeep Kaur Akhil Gupta 《Journal of Harbin Institute of Technology(New Series)》 CAS 2025年第1期67-75,共9页
Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and... Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques. 展开更多
关键词 congestion management congestion cost optimal power particle swarm flower pollination optimization
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Microseismic source location based on multi-sensor arrays and particle swarm optimization algorithm 认领 引用
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作者 LIU Ling-hao SHANG Xue-yi +2 位作者 WANG Yi LI Xi-bing FENG Fan 《Journal of Central South University》 SCIE EI CAS CSCD 2025年第9期3297-3313,共17页
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint... Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios. 展开更多
关键词 microseismic monitoring source location particle swarm optimization multi-sensor arrays
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A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization 认领 引用
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作者 Medhat A.Tawfeek Ibrahim Alrashdi +1 位作者 Madallah Alruwaili Fatma M.Talaat 《Computers, Materials & Continua》 SCIE EI 2025年第5期2773-2792,共20页
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu... Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use. 展开更多
关键词 Wireless sensor networks particle swarm optimization fuzzy multi-objective framework routing stability
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Design and optimization of weakly coupled multi-core fiber 认领 引用
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作者 ZHAN Yi WANG An +1 位作者 ZHANG Qing-long WANG Yi-han 《中国光学（中英文）》 EI CAS CSCD 北大核心 2026年第3期730-744,共15页
In order to achieve comprehensive,highly efficient,and multi-objective precise optimization of fiber structural parameters and further enhance the transmission capacity of optical communication systems,a homogeneous w... In order to achieve comprehensive,highly efficient,and multi-objective precise optimization of fiber structural parameters and further enhance the transmission capacity of optical communication systems,a homogeneous weakly coupled seven-core fiber based on trench-assisted structures is designed.Particle Swarm Optimization(PSO)is introduced to replace traditional empirical designs or local scanning methods.First,a multi-objective fitness function incorporating constraints such as dispersion,cutoff wavelength,ef-fective mode field area,and coating loss is established.Then,the algorithm performs a global search to pre-cisely determine the optimal structural parameters under standard dimensional constraints.Simulation results demonstrate that with a fiber core pitch of 45μm,the optimized fiber achieves an ultra-low inter-core crosstalk of below−90 dB/km at a wavelength of 1550 nm.This design scheme not only effectively resolves the conflict between crosstalk suppression and spatial utilization in multi-core fibers but also proves the effi-ciency and reliability of the PSO algorithm in complex fiber structural design,providing an important theor-etical basis and technical support for the research and manufacturing of ultra-large-capacity optical commu-nication systems. 展开更多
关键词 multi-core fiber particle swarm optimization trench-assisted inter-core crosstalk space divi-sion multiplexing
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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