Relevant bibliographies by topics / Particle Swarm algorithms

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Particle Swarm algorithms'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Particle Swarm algorithms"

1

Lenin, K. "CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 226–37. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1369.

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In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the d
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Dr.K., Lenin. "CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL RECATIVE POWER DISPATCH PROBLEM." International Journal of Research - Granthaalayah 6, no. 6 (2018): 226–37. https://doi.org/10.5281/zenodo.1305360.

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In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the d
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Zhang, Fan, and Zhongsheng Chen. "A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity." Symmetry 16, no. 10 (2024): 1290. http://dx.doi.org/10.3390/sym16101290.

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This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling
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Wang, H. D., C. N. Zhang, H. Zhang, Y. C. Wei, and X. L. Guan. "A Quantum Particle Swarm Optimization Algorithm Based on Aggregation Perturbation." Applied Science and Innovative Research 7, no. 4 (2023): p21. http://dx.doi.org/10.22158/asir.v7n4p21.

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A quantum particle swarm hybrid optimization algorithm based on aggregation disturbance is proposed for inventory cost control. This algorithm integrates the K-means algorithm on the basis of traditional particle swarm optimization, recalculates the clustering center, initializes stagnant particles, and solves the problem of particle aggregation. Introducing chaos mechanism into the algorithm, changing the position of particles, enhancing their activity, and improving the algorithm's global optimization ability. At the same time, define the aggregation disturbance factor, determine the current
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Liu, Hong Ying. "Utilize Improved Particle Swarm to Predict Traffic Flow." Advanced Materials Research 756-759 (September 2013): 3744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3744.

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Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effe
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Xie, Zixuan, Xueyu Huang, and Wenwen Liu. "Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy." Computational Intelligence and Neuroscience 2022 (February 23, 2022): 1–19. http://dx.doi.org/10.1155/2022/9599417.

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With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to
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Tang, Kezong, and Chengjian Meng. "Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy." Symmetry 16, no. 6 (2024): 661. http://dx.doi.org/10.3390/sym16060661.

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Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the
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Kong, Fanrong, Jianhui Jiang, and Yan Huang. "An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization." Mathematics 7, no. 6 (2019): 521. http://dx.doi.org/10.3390/math7060521.

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As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swa
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Lu, Senxiang, Jinhai Liu, Jing Wu, and Xuewei Fu. "A Fast Globally Convergent Particle Swarm Optimization for Defect Profile Inversion Using MFL Detector." Machines 10, no. 11 (2022): 1091. http://dx.doi.org/10.3390/machines10111091.

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For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected populatio
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Yan, Zheping, Chao Deng, Benyin Li, and Jiajia Zhou. "Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/672412.

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A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponent
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Dissertations / Theses on the topic "Particle Swarm algorithms"

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Sun, Yanxia. "Improved particle swarm optimisation algorithms." Thesis, Paris Est, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000395.

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D. Tech. Electrical Engineering.<br>Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.
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Brits, Riaan. "Niching strategies for particle swarm optimization." Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-02192004-143003.

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Rahman, Izaz Ur. "Novel particle swarm optimization algorithms with applications in power systems." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.

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Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods
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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.

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Gardner, Matthew J. "A Speculative Approach to Parallelization in Particle Swarm Optimization." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3012.

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Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the stand
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Kelman, Alexander. "Utilizing Swarm Intelligence Algorithms for Pathfinding in Games." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

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The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess fragmented knowledge, a concept not often utilized in games. The aim of this study is to research whether there are any benefits to using these Swarm Intelligence algorithms in comparison to standard algorithms such as A* for pathfinding in a game. Games often consist of dynamic environments with mobile agents, as such all experiments were conducted with dynamic destinations. Algorithms were measured on the length of
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Petritaj, Bajame <1989&gt. "Particle Swarm Optimization e Firework Algorithms per l’ottimizzazione di un trading system." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17108.

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Nella prima parte di questo lavoro si introduce l’analisi tecnica - lo studio del prezzo. Nella seconda parte si presentano 2 algoritmi metaeuristici: “Particle Swarm Optimization” e “Fireworks Algorithm”. Si sceglie l’algoritmo “Particle Swarm Optimization” per 3 motivi: Ergodicità – PSO ha un grado di ergodicità alto, il che significa che può ricercare spazi multi-modal con una varietà sufficiente ed allo stesso tempo evitare il local optima; Flessibilità - flessibile in quanto è semplice a coprire un grande range di problemi di ottimizzazione, problemi i quali non possono essere affrontati
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Latiff, Idris Abd. "Global-adaptive particle swarm optimisation algorithms for single and multi-objective optimisation problems." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548633.

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Zukhruf, Febri. "FREIGHT TRANSPORT NETWORK DESIGN WITH SUPPLY CHAIN NETWORK EQUILIBRIUM MODELS AND PARTICLE SWARM OPTIMISATION ALGORITHMS." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192168.

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Szöllösi, Tomáš. "Evoluční algoritmy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219654.

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The task of this thesis was focused on comparison selected evolutionary algorithms for their success and computing needs. The paper discussed the basic principles and concepts of evolutionary algorithms used for optimization problems. Author programmed selected evolutionary algorithms and subsequently tasted on various test functions with exactly the given input conditions. Finally the algorithms were compared and evaluated the results obtained for different settings.
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Books on the topic "Particle Swarm algorithms"

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Choi-Hong, Lai, and Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. CRC Press, 2011.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2010.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2013.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2013.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2010.

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COOPERATIVE PARTICLE SWARM OPTIMIZATION ALGORITHMS AND APPLICATIONS. Cayley Nielson Press, Inc., 2017.

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Lai, Choi-Hong, Jun Sun, and Xiao-Jun Wu. Particle Swarm Optimisation: Classical and Quantum Perspectives. Taylor & Francis Group, 2016.

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Lai, Choi-Hong, Jun Sun, and Xiao-Jun Wu. Particle Swarm Optimisation: Classical and Quantum Perspectives. Taylor & Francis Group, 2016.

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Sun, Jun. Particle Swarm Optimisation: Classical and Quantum Perspectives. Taylor & Francis Group, 2013.

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Ghamisi, Pedram, and Micael Couceiro. Fractional Order Darwinian Particle Swarm Optimization: Applications and Evaluation of an Evolutionary Algorithm. Springer International Publishing AG, 2015.

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Book chapters on the topic "Particle Swarm algorithms"

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Slowik, Adam. "Particle Swarm Optimization." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-20.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Particle Swarm Algorithms." In Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_8.

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Mishra, Krishn Kumar. "Particle Swarm Optimization." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-5.

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Badar, Altaf Q. H. "Particle Swarm Optimization." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-5.

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Slowik, Adam. "Particle Swarm Optimization - Modifications and Application." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-20.

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Tan, Ying, and Junqi Zhang. "Magnifier Particle Swarm Optimization." In Nature-Inspired Algorithms for Optimisation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00267-0_10.

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Zolghadr-Asli, Babak. "Particle Swarm Optimization Algorithm." In Computational Intelligence-based Optimization Algorithms. CRC Press, 2023. http://dx.doi.org/10.1201/9781003424765-7.

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Kaveh, A. "Particle Swarm Optimization." In Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05549-7_2.

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Kaveh, A. "Particle Swarm Optimization." In Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46173-1_2.

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Kaveh, Ali. "Particle Swarm Optimization." In Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59392-6_2.

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Conference papers on the topic "Particle Swarm algorithms"

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Tian, Dongping, Bingchun Li, Chen Liu, and Zulpiya Gheni. "Quantum-behaved particle swarm optimization with Lévy flight." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3059480.

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Yang, Lingyu, Ming Ni, Xinsheng Yu, and Jinzhong Wan. "Dynamic particle swarm scheduling algorithms for heterogeneous actuators." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10699126.

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Jin, Tian-Le, Qi-Jia Jiang, and Zhao-Kun Shao. "Particle Swarm Optimization-Based Algorithms for Traveling Salesman Problem." In 2025 Joint International Conference on Automation-Intelligence-Safety (ICAIS) & International Symposium on Autonomous Systems (ISAS). IEEE, 2025. https://doi.org/10.1109/icaisisas64483.2025.11052121.

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Tan, Yanxia. "An Upgraded Particle Swarm Optimization Framework for highly Efficient Robot Navigation." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020471.

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Jiao, Qinglong, Dianbin Chen, Dongfei Han, Biqi Wang, Yu Chen, and Qiuzhuang Dong. "Particle Swarm Optimization Algorithm for Target Drone Debris Recovery Task Allocation." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020050.

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Chen, Jianjie, Yanmin Liu, Yi Luo, et al. "A multi-objective particle swarm optimization algorithm for dual elite selection." In 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). IEEE, 2025. https://doi.org/10.1109/asens64990.2025.11011228.

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Jjng, Tianxu, Hailei Meng, Feng Pang, Xiaojun Dou, and Yue Liu. "Research on UAV nest assignment optimization based on particle swarm optimization algorithm." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049818.

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Jiang, Lianhua, Jianyong Zuo, Junhao Wan, Jinhu Huang, Chaodong Wu, and Jingxian Ding. "Optimization Method of Train Electric Braking Control Based on Particle Swarm Algorithm." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020116.

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Zhao, Honghua, Keyuan Zhang, Qianqian Tian, Wenguang Ren, Sai Zhang, and Jin Yu. "Manipulator Trajectory Planning and Optimization Based on Improved Particle Swarm Optimization Algorithm." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019994.

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Wang, Yuhan, Han Yu, Jiahui Ma, Nanping Li, and Lin Wang. "A Cross Strategy-Based Agent-Assisted Multi-Population Particle Swarm Optimization Algorithm." In 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). IEEE, 2025. https://doi.org/10.1109/asens64990.2025.11011206.

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Reports on the topic "Particle Swarm algorithms"

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Davis, Jeremy, Amy Bednar, and Christopher Goodin. Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm. Engineer Research and Development Center (U.S.), 2019. http://dx.doi.org/10.21079/11681/34160.

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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.

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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with perform
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