Relevant bibliographies by topics / Modified particle searm optimization

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Academic literature on the topic 'Modified particle searm optimization'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Modified particle searm optimization"

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Chin, Wen Chuang, Yu Chen Kuan, and Hua Su Yi. "Identifying the Slider Crank Mechanism System by the MPSO Method." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 181–88. https://doi.org/10.31142/ijtsrd18413.

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This paper mainly proposes an efficient modified particle swarm optimization MPSO method, to identify a slider crank mechanism driven by a field oriented PM synchronous motor, dynamic formulations of a slider crank mechanism have been successfully formulated with only one independent variable. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider crank mechanism. This new algorithm is added with "distance" term in the traditional PSO's fitness
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Wang, Tianpeng, Mingcheng Sun, Yunan Wang, Zhongyu Wang, Yongfeng Xia, and Jie Zhang. "Optimization of the active element and reinforcement proportion for improving joint strength of brazed Si3N4/42CrMo." Journal of Physics: Conference Series 2713, no. 1 (2024): 012003. http://dx.doi.org/10.1088/1742-6596/2713/1/012003.

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Abstract Micron-TiN particles and Ti powder were introduced into the AgCu powder filler to design a particle-reinforced composite filler. This TiN particle (TiNp) modified brazing material was adopted to reliably braze Si3N4 ceramics onto 42CrMo steel, and the ratio of active element Ti to TiNp reinforcement was optimized for joint strength improvement. The reaction layer’s thickness (TiN+Ti5Si3) adjacent to the ceramic decreased by introducing TiNp. Nevertheless, the reaction layer’s thickness (TiC) at the 42CrMo side did not vary too much. TiNp showed excellent wettability with Ag-Cu-Ti braz
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Xu, Xing, Xingzhi Wang, and Yuanzhi Li. "Prediction of the Height of Water Flowing Fractured Zone Based on the MPSO-BP Neural Network Model." Mathematical Problems in Engineering 2022 (August 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/2133695.

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The height of the water flowing fractured zone (HWFFZ) is the key parameter for safe coal mining under water bodies. In order to improve the accuracy of prediction model of the HWFFZ, the grey relational analysis (GRA) is used to quantitatively analyze the influencing factors of the HWFFZ, the main influencing factors of the HWFFZ under the condition of comprehensive mechanized mining and comprehensive top coal caving mining are selected scientifically, and these five factors that include mining depth, mining thickness, inclined length of working face, hard rock lithology proportion coefficien
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Lee, Sangwook, Sangmoon Soak, Sanghoun Oh, Witold Pedrycz, and Moongu Jeon. "Modified binary particle swarm optimization." Progress in Natural Science 18, no. 9 (2008): 1161–66. http://dx.doi.org/10.1016/j.pnsc.2008.03.018.

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ChengHong Yang, ShengWei Tsai, LiYeh Chuang, and ChengHuei Yang. "A Modified Particle Swarm Optimization for Global Optimization." International Journal of Advancements in Computing Technology 3, no. 7 (2011): 169–89. http://dx.doi.org/10.4156/ijact.vol3.issue7.22.

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Mu, Ai-Qin, De-Xin Cao, and Xiao-Hua Wang. "A Modified Particle Swarm Optimization Algorithm." Natural Science 01, no. 02 (2009): 151–55. http://dx.doi.org/10.4236/ns.2009.12019.

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Zhang, Zhe, Limin Jia, and Yong Qin. "Modified constriction particle swarm optimization algorithm." Journal of Systems Engineering and Electronics 26, no. 5 (2015): 1107–13. http://dx.doi.org/10.1109/jsee.2015.00120.

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Dahunsi, Olurotimi A., Muhammed Dangor, Jimoh O. Pedro, and M. Montaz Ali. "Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 2 (2019): 393–415. http://dx.doi.org/10.1177/1461348419842676.

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Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swa
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Jain, Khushboo, and Anuradha Purohit. "Feature Selection using Modified Particle Swarm Optimization." International Journal of Computer Applications 161, no. 7 (2017): 8–12. http://dx.doi.org/10.5120/ijca2017913229.

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Karim, Aasam Abdul, Nor Ashidi Mat Isa, and Wei Hong Lim. "Modified Particle Swarm Optimization With Effective Guides." IEEE Access 8 (2020): 188699–725. http://dx.doi.org/10.1109/access.2020.3030950.

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Dissertations / Theses on the topic "Modified particle searm optimization"

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Alrasheed, Mohammed R. A. "A modified particle swarm optimization and its application in thermal management of an electronic cooling system." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37900.

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Particle Swarm Optimization (PSO) is an evolutionary computation technique, which has been inspired by the group behavior of animals such as schools of fish and flocks of birds. It has shown its effectiveness as an efficient, fast and simple method of optimization. The applicability of PSO in the design optimization of heat sinks is studied in this thesis. The results show that the PSO is an appropriate optimization tool for use in heat sink design.PSO has common problems that other evolutionary methods suffer from. For example, in some cases premature convergence can occur where particles ten
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Wei, Xing. "Optimization of Strongly Nonlinear Dynamical Systems Using a Modified Genetic Algorithm With Micro-Movement (MGAM)." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/450.

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The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while
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Zemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.

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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluat
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Li, Nai-Jen, and 黎乃仁. "Weighted Particle Based Modified Particle Swarm Optimization and Its Applications." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/75030636258994420520.

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博士<br>國立中央大學<br>電機工程學系<br>103<br>This dissertation proposes weighted particle based modified particle swarm optimization (PSO) and its applications. The weighted particle is composed of personal best particles with their best objective value (minimal value), and the weighted particle can provide better opportunity getting closer to the optimal solution. Based on weighted particle, this dissertation proposes three modified algorithms. One is an enhanced particle swarm optimization with weighted particle (EPSOWP) which has two search behaviors of local search and global search so that the swarm
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Wang, Ya-shyan, and 王雅賢. "Research on a Modified Particle Swarm Optimization Algorithm." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/62188914837148750779.

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碩士<br>中原大學<br>資訊管理研究所<br>94<br>Particle Swarm Optimization (PSO), an algorithm with the concept of swarm intelligence, also a new branch in evolutionary computing, possesses the merits of fast converging, as well as the simplification in parameter setting. Nevertheless, PSO has the demerit of the inclination to trap into local optima because when particles move, they merely follow pbest and gbest .Although the standard PSO algorithm and other modified algorithms has attempted to enhance the efficiency in utilizing a swarm to search global best, they still fail in avoiding particles falling int
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Liao, Chun-Ming, and 廖俊明. "Applying Modified Particle Swarm Optimization for Finding Motif." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/97787732133655995979.

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碩士<br>義守大學<br>工業工程與管理學系碩士班<br>98<br>Remarkable advances on human genome project and the molecular biology provide availability of genome-wide gene expression data. Gene activity and regulation mechanism is often affected by binding transcription factors to short fragments in DNA sequences called motifs. The motif is significant pattern of letters (nucleotides, amino acids) contained within long sequences. Identification of subtle regulatory motifs in a DNA or protein sequence is a difficult pattern recognition problem in genetics. Motif finding is also an important area of multiple sequence al
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Li, Jung-Chieh, and 李榮結. "Modified Particle Swarm Optimization for High Dimensional Problems." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/69494731072811920445.

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碩士<br>國立臺灣海洋大學<br>商船學系所<br>99<br>The difficulty to solve a high dimensional optimization problem will be much more than low dimensional problems due to the increasing of solution space. The Particle Swarm Optimization (PSO) has been applied extensively to various fields.. However, there are weaknesses in adopting PSO in high dimensional problems. For examples, PSO is easily trapped into local extreme resulting in low optimizing precision or failure, and even though in the surroundings of extreme solution, the rate of convergence need to be improved. This study is focused on the performance o
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Ying-Ju, Ho. "Construct D-Optimal Designs Using Modified Particle Swarm Optimization." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-2607200615310300.

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Ho, Ying-Ju, and 何英如. "Construct D-Optimal Designs Using Modified Particle Swarm Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/49523377318970366187.

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碩士<br>元智大學<br>工業工程與管理學系<br>94<br>Optimal designs are applied extensively to the process industry. Asymmetrical designs are now widely used in practice since many engineering problems usually involve complex objective functions and several constraints simultaneously. Hence, the classical designs cannot be valid for this circumstance any more. Accordingly, computer-generated designs are the legitimate choice for situations where standard factorial, fractional factorial designs or response surface designs cannot be easily employed. Design optimality criteria are characterized by letters of the al
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Yi-Yin, Chiu. "Modified Particle Swarm Optimization for Solving the Global Optimization of Continuous Multimodal Functions." 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611323569.

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Book chapters on the topic "Modified particle searm optimization"

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Zhang, Dazhi, and Zetao Shao. "Modified Particle Swarm Optimization Algorithm Based on Particle Classification." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8944-2_47.

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Priyavada and Binay Kumar. "A Review of Modified Particle Swarm Optimization Method." In Soft Computing and Optimization. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6406-0_3.

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K.P, Swetha, and V. Susheela Devi. "Modified Particle Swarm Optimization for Pattern Clustering." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_60.

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Sun, Jun, C. H. Lai, Wenbo Xu, Yanrui Ding, and Zhilei Chai. "A Modified Quantum-Behaved Particle Swarm Optimization." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72584-8_38.

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Cheng, Wy-Liang, Koon Meng Ang, Zhi Chuan Choi, et al. "Particle Swarm Optimization with Modified Initialization Scheme for Numerical Optimization." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8690-0_45.

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Wang, Ling, Mingde Zhang, Qun Niu, and Jun Yao. "A Modified Quantum-Inspired Particle Swarm Optimization Algorithm." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_51.

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Kundu, Rupam, Rohan Mukherjee, and Swagatam Das. "Modified Particle Swarm Optimization with Switching Update Strategy." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_75.

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Sarangi, Archana, Shubhendu Kumar Sarangi, Bibhuprasad Mohanty, and Shweta Prasad Bhusal. "Gaussian Mutated Particle Swarm Optimization with Modified Velocity for Function Optimization." In ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8461-5_26.

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Wang, Ling, Wei Ye, Xiping Fu, and Muhammad Ilyas Menhas. "A Modified Multi-objective Binary Particle Swarm Optimization Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_6.

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Ghosh, Pradipta, Hamim Zafar, and Ankush Mandal. "Modified Local Neighborhood Based Niching Particle Swarm Optimization for Multimodal Function Optimization." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_25.

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Conference papers on the topic "Modified particle searm optimization"

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Wu, Chunyan, Ling Zhou, Fuqiang Peng, Pande Jiao, Dayong Zhou, and Qianlong Mei. "A Modified Particle Swarm Optimization Algorithm." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862446.

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Ratanavilisagul, Chiabwoot. "Modified Particle Swarm Optimization for Solving Handwritten Digits." In 2024 4th International Conference on Computer Communication and Information Systems (CCCIS). IEEE, 2024. http://dx.doi.org/10.1109/cccis63483.2024.00017.

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Dhurve, Rajesh Kumar, Shilpi Tomar, Shivendra Singh Thakur, and C. S. Sharma. "Economic And Emission Load Dispatch Using Modified Particle Swarm Optimization." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940259.

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Zhigang Zhou. "Modified particle swarm optimization for unconstrained optimization." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451219.

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Zhihua, Cui, Cai Xingjuan, Zeng Jianchao, and Sun Guoji. "Predictive-Velocity Modified Particle Swarm Optimization." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4347338.

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Yitong, Liu, Fu Mengyin, and Gao Hongbin. "A Modified Particle Swarm Optimization Algorithm." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4347362.

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Liu, Enhai, Yongfeng Dong, Jie Song, Xiangdan Hou, and Nana Li. "A Modified Particle Swarm Optimization Algorithm." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.355.

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Qian-Li Zhang, Xing Li, and Quang-Ahn Tran. "A modified particle swarm optimization algorithm." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527455.

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Yan, Jiang, Hu Tiesong, Huang Chongchao, and Wu Xianing. "A Modified Particle Swarm Optimization Algorithm." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.294167.

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Kurniawan, Muchamad, Rani Muhima, Maftahatul Hakimah, Siti Agustini, and Rahmi Putri. "Modified Particle Swarm Optimization for Clustering." In International Conference on Advanced Engineering and Technology. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012111800003680.

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