Relevant bibliographies by topics / Particle Swarm Algorithm (PSA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Particle Swarm Algorithm (PSA)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Particle Swarm Algorithm (PSA)"

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Zhang, Yan Sheng, Zhong Tao Qiao, and Jian Hui Jing. "Diagnostic Strategy Optimization Based on Particle Swarm Algorithm." Applied Mechanics and Materials 215-216 (November 2012): 555–60. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.555.

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Fault diagnostic strategy profoundly influences diagnostic efficiency and cost. Diagnostic strategy optimization is a Non-Polynomial optimizing problem, which is a hard one. Applying conventional methods to resolving it, there are some difficulties: more complex to implementing algorithm, more time to diagnosing and more difficult to attaining global optimum. Particle swarm algorithm (PSA) is a new intelligent optimization algorithm, and applied to optimizing diagnostic strategy. Function of all-in cost is constructed by state probability, isolating matrix and test cost, serving as objective f
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Engr., Muhammad Zakria, and Muhammad Naeem Arbab Dr. "Design and Implementation of Power System Optomization Using Particles Swarm Algorithms for Addressing the Economic Dispatch Problem." International Journal of Engineering Works (ISSN:2409-2770) 4, no. 8 (2017): 151–55. https://doi.org/10.5281/zenodo.887437.

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This research is aimed to design a power system using particle swarm algorithm (PSA) and test its efficiency on a standard IEEE bus bar system. The algorithm has been modified for power generating system and successfully demonstrates and provides optimization results for six generating units . Reducion in fuel cost by distributing load among the generating units in inter connected bus bar system is the core area of this research. The losses in power transmission as well as generation are also minimized. Moreover, the PSA coding was executed using MATLAB and the graphs are shown for comparison
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Fang, Bin. "Blockchain-Based Educational Management and Secure Software-Defined Networking in Smart Communities." International Journal of Information Security and Privacy 16, no. 2 (2022): 1–20. http://dx.doi.org/10.4018/ijisp.308314.

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As network multimedia technology has advanced rapidly, video communication has made enormous strides and has been used in many educational industries. The use of educational video communication is a simple, effective, and convenient method of communication. A fast and energy-efficient blockchain clustering algorithm is proposed to pick the desired cluster head in software-defined networking (SDN). This research suggests using an adaptive resource allocation method and secure communication networks to improve the quality of instructional video transmission. To better educational management reso
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Nancy, Mubina, and Elizabeth Amudhini Stephen. "Modelling and Analysis of the Cone Coupling Problem Using Optimization." Mathematical Modelling of Engineering Problems 9, no. 5 (2022): 1385–92. http://dx.doi.org/10.18280/mmep.090529.

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A coupling is a mechanism that transmits operative power between two shafts that are revolving at different speeds. A coupling connects two shafts at their ends and can slip or fail depending on the torque limit. It is an essential component of any power transmission system and may survive for a very long time if properly designed and maintained. This study's current research, a newly developed optimization algorithms are used to minimize the volume of cone coupling. The current study presented here compares modern metaheuristic methods for optimizing the design of the cone coupling problem. T
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Mohammadi, Arezoo, Mehrzad Minnoei, Zadollah Fathi, Mohamamd Ali Keramati, and Hossein Baktiari. "Optimal allocation of bank resources and risk reduction through portfolio decentralization." International Journal of Economic Sciences 11, no. 2 (2022): 92–143. http://dx.doi.org/10.52950/es.2022.11.2.007.

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The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, w
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Ab. Aziz, Nor Azlina, and Kamarulzaman Ab. Aziz. "Pendulum Search Algorithm: An Optimization Algorithm Based on Simple Harmonic Motion and Its Application for a Vaccine Distribution Problem." Algorithms 15, no. 6 (2022): 214. http://dx.doi.org/10.3390/a15060214.

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The harmonic motion of pendulum swinging centered at a pivot point is mimicked in this work. The harmonic motion’s amplitude at both side of the pivot are equal, damped, and decreased with time. This behavior is mimicked by the agents of the pendulum search algorithm (PSA) to move and look for an optimization solution within a search area. The high amplitude at the beginning encourages exploration and expands the search area while the small amplitude towards the end encourages fine-tuning and exploitation. PSA is applied for a vaccine distribution problem. The extended SEIR model of Hong Kong’
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Shi, Kuang, and Tingting Sun. "Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application." Buildings 15, no. 13 (2025): 2216. https://doi.org/10.3390/buildings15132216.

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This study employs a PID (Proportion, Integral, Differential)-based search algorithm (PSA) to achieve structural damage identification (SDI), localization, and quantification. We developed finite element programs for a 10-element simply supported beam, a 21-element truss, and a 7-story steel frame, assigning damage factors to each element as design variables. The Relative Frequency Change Rate (RFCR) and Modal Assurance Criterion (MAC) were calculated as objective functions for PSA iteration; comparative studies were then conducted against Particle Swarm Optimization (PSO), Genetic Algorithm (
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Chen, Dong, Bin Lin, Zhi Lin Han, and Yan Bin Zhang. "Cutting Parameter Optimization of Stability in Cylindrical Shell Based on Particle Swarm Algorithm." Advanced Materials Research 662 (February 2013): 670–73. http://dx.doi.org/10.4028/www.scientific.net/amr.662.670.

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For cylindrical shell, because of its structure characteristic (i.e. long and thin-walled), it has low stiffness, bad fabrication procedure. And it’s very easy to cause vibration in the cutting process, especially the type of regenerative vibration. So, it’s vital for the surface quality of work-piece and cutting stability to select cutting parameter reasonably. The paper proposed a dynamic optimization method, which aimed at maximization of material removal rate. This method could guarantee the maximum material removal rate under the condition of cutting stability based on Particle Swarm Algo
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Huang, JinDian, YingHan Hong, and KeYe Lin. "Dynamic scheduling strategy and algorithm for mixed batch scheduling in vacuum freeze-dried fruit processes." Production Engineering Archives 30, no. 4 (2024): 477–90. http://dx.doi.org/10.30657/pea.2024.30.45.

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Abstract Vacuum freeze-dried fruit processes consisting of heating and holding are modelled as a mixed batch scheduling with the objective of minimizing the makespan. The jobs differ from each other in job family, size, weight and ready time. The batch processing time is determined by the longest job and the total weight of the jobs in the batch. A mixed-integer linear programming model is developed and tested with small-scale examples. Typical batch scheduling strategies are analysed and a machine-based dynamic programming strategy is proposed. The machine-based dynamic scheduling strategy is
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Huang, Quanzhen, Suxia Chen, Huayan Pu, and Ning Zhang. "Optimal Piezoelectric Actuators and Sensors Configuration for Vibration Suppression of Aircraft Framework Using Particle Swarm Algorithm." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/7213125.

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Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA). Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators i
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Dissertations / Theses on the topic "Particle Swarm Algorithm (PSA)"

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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the a
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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.

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Sholedolu, Michael O. "Nature-inspired optimisation : improvements to the Particle Swarm Optimisation Algorithm and the Bees Algorithm." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/55013/.

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This research focuses on nature-inspired optimisation algorithms, in particular, the Particle Swarm Optimisation (PSO) Algorithm and the Bees Algorithm. The PSO Algorithm is a population-based stochastic optimisation technique first invented in 1995. It was inspired by the social behaviour of birds flocking or a school of fish. The Bees Algorithm is a population-based search algorithm initially proposed in 2005. It mimics the food foraging behaviour of swarms of honey bees. The thesis presents three algorithms. The first algorithm called the PSO-Bees Algorithm is a cross between the PSO Algori
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Sallama, Abdulhafid Faraj. "Multi particle swarm optimisation algorithm applied to supervisory power control systems." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/10195.

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Power quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the
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Maripi, Jagadish Kumar. "AN EFFECTIVE PARALLEL PARTICLE SWARM OPTIMIZATION ALGORITHM AND ITS PERFORMANCE EVALUATION." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/275.

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Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. W
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Phung, Nhu Kien <1987&gt. "Optimization of an HVAC system with a particle swarm optimization algorithm." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8062.

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This paper addresses the issues of energy consumption reduction in existing buildings while providing comfort levels for inhabitants. Beside, representing an information-driven for the advancement of an HVAC(heating, ventilation, and air conditioning) system in an office building. The core of this work will be the application on a real case study (an office building) in which the mentioned objectives depend on a set of independent and control variables such as internal temperature, humidity, and the presence of people. In particular, this research will find the best configurations of the cont
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Harrison, Kyle Robert. "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/66103.

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The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phas
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Silwal, Shrawani. "A Dynamic Taxi Ride Sharing System Using Particle Swarm Optimization." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1588198872893409.

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Wilke, Daniel Nicolas. "Analysis of the particle swarm optimization algorithm." Diss., 2005. http://hdl.handle.net/2263/26201.

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Increasing prominence is given to the role of optimization in engineering. The global optimization problem is in particular frequently studied, since this difficult optimization problem is in general intractable. As a result, many a solution technique have been proposed for the global optimization problem, e.g. random searches, evolutionary computation algorithms, taboo searches, fractional programming, etc. This study is concerned with the recently proposed zero-order evolutionary computation algorithm known as the particle swarm optimization algorithm (PSOA). The following issues are address
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Chen, Shiau-Kai, and 陳孝楷. "Multimodal Engineering Optimization Using Particle Swarm Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/73488226343982594528.

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碩士<br>淡江大學<br>機械與機電工程學系碩士班<br>98<br>A theoretical hypothesis was made by the inspiration of searching food and flying behaviors of bird colony, a synchronous searching algorithm solving multiple local optimums has been developed in this thesis. Individual bird is defined as a particle without the mass and volume. Such a group particles together food searching with a suitable algorithm is particle swarm optimization (PSO). Multiple food locations corresponding to multiple local optimums can be obtained by multiple population of birds’ colonial parallel search. A niche strategy used on genetic a
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Books on the topic "Particle Swarm Algorithm (PSA)"

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

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López, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.

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Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida
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Book chapters on the topic "Particle Swarm Algorithm (PSA)"

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Hendtlass, Tim. "The Particle Swarm Algorithm." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78293-3_23.

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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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Chen, Teng-Bo, Yin-Li Dong, Yong-Chang Jiao, and Fu-Shun Zhang. "Crossed Particle Swarm Optimization Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_123.

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Cuevas, Erik, and Alma Rodríguez. "Particle Swarm Optimization (PSO) Algorithm." In Metaheuristic Computation with MATLAB®. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-6.

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Lebedev, Oleg B., Olga A. Purchina, and Dmitriy D. Fugarov. "Modified Adaptive Particle Swarm Algorithm." In Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43789-2_18.

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Ge, Yang, and Zhang Rubo. "An Emotional Particle Swarm Optimization Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_67.

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Chang, Chunguang, and Xi Wu. "An Improved Particle Swarm Optimization Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_195.

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Kim, D. H., Ajith Abraham, and K. Hirota. "Hybrid Genetic: Particle Swarm Optimization Algorithm." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_7.

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Pan, Dazhi, and Zhibin Liu. "An Improved Particle Swarm Optimization Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24282-3_76.

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Zhang, Jiapu. "Particle Swarm Global Optimization Search Algorithm." In Molecular Structures and Structural Dynamics of Prion Proteins and Prions. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7318-8_17.

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Conference papers on the topic "Particle Swarm Algorithm (PSA)"

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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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Qadikolai, Mahdie Hasanpour. "Frequency control of isolated hybride power netwotk using genethic algorithm and particle swarm optimization." In 2015 30th International Power System Conference (PSC). IEEE, 2015. http://dx.doi.org/10.1109/ipsc.2015.7827746.

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Maleki, Akbar, and Fathollah Pourfayaz. "Optimization of grid independent diesel-based hybrid system for power generation using improved particle swarm optimization algorithm." In 2015 30th International Power System Conference (PSC). IEEE, 2015. http://dx.doi.org/10.1109/ipsc.2015.7827735.

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Mamat, Nor Hana, Samsul Bahari Mohd Noor, Azura Che Soh, Ahmad Hazri Ab Rashid, Nur Liyana Jufika Ahmad, and Ishak Mohd Yusoff. "Optimization of Neural Network Architecture using Particle Swarm Algorithm for Dissolved Oxygen Modelling in a 200L Bioreactor PHA Production." In 2018 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2018. http://dx.doi.org/10.1109/scored.2018.8711233.

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Bai, Ming-Ming, Hui Sun, Lie-Yang Wu, Ze-Tao Jiang, and Wen-Huan Wu. "Three Sub-Swarm Particle Swarm Optimization Algorithm." In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 2009. http://dx.doi.org/10.1109/iwisa.2009.5073219.

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Koohi, Iraj, and Voicu Z. Groza. "Optimizing Particle Swarm Optimization algorithm." In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2014. http://dx.doi.org/10.1109/ccece.2014.6901057.

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Xiaojing, Yang, Jiao Qingju, and Liu Xinke. "Center Particle Swarm Optimization Algorithm." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729510.

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Wang, Gai-Ge, Danyu Bai, Wenyin Gong, Teng Ren, Xiaobo Liu, and Xuesong Yan. "Particle-swarm Krill Herd Algorithm." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607812.

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Gao, Ye, and Shan Li. "Improved Particle Swarm Optimization Algorithm." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677054.

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Chuanjun, Wen, Wang Ling, and Ren Xuejing. "General particle swarm optimization algorithm." In 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. http://dx.doi.org/10.1109/eebda56825.2023.10090725.

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Reports on the topic "Particle Swarm Algorithm (PSA)"

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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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