Literatura académica sobre el tema "Discrete Particle Swarm Optimization"
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Artículos de revistas sobre el tema "Discrete Particle Swarm Optimization"
Roy, Rahul, Satchidananda Dehuri y Sung Bae Cho. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem". International Journal of Applied Metaheuristic Computing 2, n.º 4 (octubre de 2011): 41–57. http://dx.doi.org/10.4018/jamc.2011100104.
Texto completoXiao, Bin y Zhao Hui Li. "An Improved Hybrid Discrete Particle Swarm Optimization Algorithm to Solve the TSP Problem". Applied Mechanics and Materials 130-134 (octubre de 2011): 3589–94. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3589.
Texto completoTing, T. O., H. C. Ting y T. S. Lee. "Taguchi-Particle Swarm Optimization for Numerical Optimization". International Journal of Swarm Intelligence Research 1, n.º 2 (abril de 2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.
Texto completoWang, Bei Zhan, Xiang Deng, Wei Chuan Ye y Hai Fang Wei. "Study on Discrete Particle Swarm Optimization Algorithm". Applied Mechanics and Materials 220-223 (noviembre de 2012): 1787–94. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1787.
Texto completoKang, Qi, Lei Wang y Qidi Wu. "Swarm-based approximate dynamic optimization process for discrete particle swarm optimization system". International Journal of Bio-Inspired Computation 1, n.º 1/2 (2009): 61. http://dx.doi.org/10.1504/ijbic.2009.022774.
Texto completoBeheshti, Zahra, Siti Mariyam Shamsuddin y Shafaatunnur Hasan. "Memetic binary particle swarm optimization for discrete optimization problems". Information Sciences 299 (abril de 2015): 58–84. http://dx.doi.org/10.1016/j.ins.2014.12.016.
Texto completoSarathambekai, S. y K. Umamaheswari. "Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem". Journal of Algorithms & Computational Technology 11, n.º 1 (19 de septiembre de 2016): 58–67. http://dx.doi.org/10.1177/1748301816665521.
Texto completoZhang, Jun Ting y Li Xia Qiao. "Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO". Advanced Materials Research 681 (abril de 2013): 130–36. http://dx.doi.org/10.4028/www.scientific.net/amr.681.130.
Texto completoWu, Yanmin y Qipeng Song. "Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration". Mathematical Problems in Engineering 2021 (11 de marzo de 2021): 1–10. http://dx.doi.org/10.1155/2021/5574501.
Texto completoR. B., Madhumala, Harshvardhan Tiwari y Devaraj Verma C. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization". International Journal of Cloud Applications and Computing 12, n.º 2 (1 de abril de 2022): 1–12. http://dx.doi.org/10.4018/ijcac.305856.
Texto completoTesis sobre el tema "Discrete Particle Swarm Optimization"
Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis". University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.
Texto completoMuthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems". Diss., Online access via UMI:, 2009.
Buscar texto completoAminbakhsh, Saman. "Hybrid Particle Swarm Optimization Algorithm For Obtaining Pareto Front Of Discrete Time-cost Trade-off Problem". Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615398/index.pdf.
Texto completoDevarakonda, SaiPrasanth. "Particle Swarm Optimization". University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335827032.
Texto completoAl-kazemi, Buthainah Sabeeh No'man. "Multiphase particle swarm optimization". Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
Texto completoJUNQUEIRA, Caio Marco dos Santos. "Um algoritmo para alocação ótima de detectores de afundamentos de tensão". Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/479.
Texto completoMade available in DSpace on 2018-04-24T17:30:10Z (GMT). No. of bitstreams: 1 CAIO MARCO DOS SANTOS JUNQUEIRA – DISSERTAÇÃO (PPGEE) 2017.pdf: 6011061 bytes, checksum: 25c9c9fad6015613e54aae9e700918af (MD5) Previous issue date: 2017-03-09
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Um algoritmo para alocação ótima de detectores de afundamentos de tensão (AT) é apresentado nesta dissertação. O algoritmo utiliza a Transformada Wavelet Discreta (TWD) paraa detecção de AT e o conceito de Matriz de Observabilidade Topológica (MOT) para avaliar o desempenho dos Sistemas de Distribuição de Energia Elétrica (SDEE) quando submetidos à tais distúrbios. Para resolver o problema de alocação ótima dos dispositivos detectores de AT, utilizou-se o método Binary Particle Swarm Optimi- tization (BPSO). Adicionalmente, apresenta-se uma metodologia de criação de uma base de dados para geração automática de AT. O algoritmo foi avaliado considerando-se dois sistemas: um sistema-testedo IEEE e um SDEE que simula um alimentador real da cidade de BoaVista-PB, os quais foram simulados no software Alternative Transient Pro- gram (ATP). Osresultados obtidos indicaram que o algoritmo é capaz de detectar AT em todo o sistema, fazendo o uso da instalação de detectores em poucas barras, oque indubitavelmente, reduzirá o custo do sistema de monitoramento.
An algorithm for optimal placement of voltage sags (VS) detectors is presented in this dissertation. The algorithm uses the Discrete Wavelet Transform (DWT) for VS detection and the Topological Observability Matrix (TOM) concept to evaluate the per- formance of the Electric Power Distribution Systems (EPDS) when subjected to such disturbances. In order to solve the problem of optimal placement of the VS detecting devices, the Binary Particle Swarm Optimization (BPSO) method was used. Additionally, a methodology for the creation of a database for automatic VS generation is presented. The algorithm was evaluated considering two systems: an IEEE test system and a EPDS that simulates a real feeder in Boa Vista-PB city, which were simulated in the Alternative Transient Program (ATP) software. The results indicate that the algorithm is capable of detecting VS throughout the system, using the installation of detectors in a few buses, which will undoubtedly reduce the cost of the monitoring system.
Bernardes, Wellington Maycon Santos. "Algoritmo enxame de partículas discreto para coordenação de relés direcionais de sobrecorrente em sistemas elétricos de potência". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-14052013-094113/.
Texto completoThis work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.
Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer". Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.
Texto completoThesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
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Karlberg, Hampus. "Task Scheduling Using Discrete Particle Swarm Optimisation". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287311.
Texto completoOptimering av arbetsfördelning i nätverk kan öka användandet av tillgängliga resurser. I instabila heterogena nätverk kan schemaläggning användas för att optimera beräkningstid, energieffektivitet och systemstabilitet. Då nätverk består av sammankopplade resurser innebär det också att vad som är ett optimalt schema kan komma att ändras över tid. Bredden av nätverkskonfigurationer gör också att det kan vara svårt att överföra och applicera ett schema från en konfiguration till en annan. Diskret Particle Swarm Optimisation (DPSO) är en meta heuristisk metod som kan användas för att ta fram lösningar till schemaläggningsproblem. Den här uppsatsen kommer utforska hur DPSO kan användas för att optimera schemaläggning för instabila nätverk. Syftet är att hitta en lösning för nätverk under liknande begränsningar som de som återfinns på tåg. Detta för att i sin tur facilitera planerandet av optimala banor. Genom användandet av ett artificiellt neuralt nätverk (ANN) uppskattar vi schemaläggningskostnaden. Denna kostnad används sedan av DPSO heuristiken för att utforska en lösningsrymd med potentiella scheman. Våra resultat fokuserar på optimeringen av grupperingsstorleken av distribuerade problem i relation till robusthet och letens. Vi simulerar ett flertal instabila och heterogena nätverk och jämför deras prestanda. Utgångspunkten för jämförelsen är schemaläggning där uppgifter distribueras jämnt i bestämda gruperingsstorlekar. Prestandan analyseras sedan i relation till användbarheten i verkliga scenarion. Våra resultat visar på en signifikant ökning i prestanda inom ett brett spann av nätverkskonfigurationer. Det här är på bekostnad av långa söktider för DPSO algoritmen. Vår slutsats är att under rätt förutsättningar kan metoden användas för att snabba upp distribuerade beräkningar förutsatt att beräkningarna för schemaläggningen görs i förväg. Vi rekommenderar vidare utforskande av DPSO algoritmens parametrar för att snabba upp konvergens, samt undersökande av algoritmens prestanda i verkliga miljöer.
Czogalla, Jens. "Particle swarm optimization for scheduling problems". Aachen Shaker, 2010. http://d-nb.info/1002307813/04.
Texto completoLibros sobre el tema "Discrete Particle Swarm Optimization"
Lazinica, Aleksandar. Particle swarm optimization. Rijek, Crotia: InTech, 2009.
Buscar texto completoMercangöz, Burcu Adıgüzel, ed. Applying Particle Swarm Optimization. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6.
Texto completoCouceiro, Micael y Pedram Ghamisi. Fractional Order Darwinian Particle Swarm Optimization. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19635-0.
Texto completoMikki, Said M. y Ahmed A. Kishk. Particle Swarm Optimization: A Physics-Based Approach. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01704-9.
Texto completoOlsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Hauppauge, N.Y: Nova Science Publishers, 2010.
Buscar texto completo1974-, Parsopoulos Konstantinos E. y Vrahatis Michael N. 1955-, eds. Particle swarm optimization and intelligence: Advances and applications. Hershey, PA: Information Science Reference, 2010.
Buscar texto completoParsopoulos, Konstantinos E. Particle swarm optimization and intelligence: Advances and applications. Hershey, PA: Information Science Reference, 2010.
Buscar texto completoKiranyaz, Serkan, Turker Ince y Moncef Gabbouj. Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37846-1.
Texto completoChoi-Hong, Lai y Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. Boca Raton: CRC Press, 2011.
Buscar texto completoClerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2010.
Buscar texto completoCapítulos de libros sobre el tema "Discrete Particle Swarm Optimization"
Liu, QingFeng. "An Improved Discrete Particle Swarm Optimization Algorithm". En Lecture Notes in Electrical Engineering, 883–90. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4853-1_108.
Texto completoKarthi, R., S. Arumugam y K. Ramesh Kumar. "Discrete Particle Swarm Optimization Algorithm for Data Clustering". En Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), 75–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03211-0_7.
Texto completoAkhand, M. A. H., Md Masudur Rahman y Nazmul Siddique. "Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems". En Studies in Computational Intelligence, 59–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09835-2_4.
Texto completoTao, Qian, Hui-you Chang, Yang Yi, Chun-qin Gu y Wen-jie Li. "A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization". En Lecture Notes in Computer Science, 166–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_22.
Texto completoClerc, Maurice. "Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem". En New Optimization Techniques in Engineering, 219–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_8.
Texto completoGarg, Ritu y Awadhesh Kumar Singh. "Multi-objective Workflow Grid Scheduling Based on Discrete Particle Swarm Optimization". En Swarm, Evolutionary, and Memetic Computing, 183–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_23.
Texto completoZhan, Zhi-hui y Jun Zhang. "Discrete Particle Swarm Optimization for Multiple Destination Routing Problems". En Lecture Notes in Computer Science, 117–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01129-0_15.
Texto completoYuan, Ying, Cuirong Wang, Cong Wang, Shiming Zhu y Siwei Zhao. "Discrete Particle Swarm Optimization Algorithm for Virtual Network Reconfiguration". En Lecture Notes in Computer Science, 250–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38703-6_30.
Texto completoBui, Ha-Duong, Sungmoon Jeong, Nak Young Chong y Matthew Mason. "Origami Folding Sequence Generation Using Discrete Particle Swarm Optimization". En Neural Information Processing, 484–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_51.
Texto completoZhang, Kai, Wanying Zhu, Jun Liu y Juanjuan He. "Discrete Particle Swarm Optimization Algorithm for Solving Graph Coloring Problem". En Communications in Computer and Information Science, 643–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-49014-3_57.
Texto completoActas de conferencias sobre el tema "Discrete Particle Swarm Optimization"
Sarif, Bambang A. B. y Mostafa Abd-El-Barr. "Functional synthesis using discrete particle swarm optimization". En 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668306.
Texto completoEngelbrecht, A. P. "Asynchronous particle swarm optimization with discrete crossover". En 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011788.
Texto completoXu, Yufa, Guochu Chen y Jinshou Yu. "Three Sub-Swarm Discrete Particle Swarm Optimization Algorithm". En 2006 IEEE International Conference on Information Acquisition. IEEE, 2006. http://dx.doi.org/10.1109/icia.2006.305922.
Texto completoEngelbrecht, AP. "Particle swarm optimization with discrete crossover". En 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557864.
Texto completoWang, Xin, Xing Wang y Na Li. "Discrete local particle swarm optimization: A more rapid and precise hybrid particle swarm optimization". En 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818030.
Texto completoLu, Qiang, Qing-He Xu y Xue-Na Qiu. "Discrete Particle Swarm Optimization with Chaotic Initialization". En 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162645.
Texto completoStrasser, Shane, Rollie Goodman, John Sheppard y Stephyn Butcher. "A New Discrete Particle Swarm Optimization Algorithm". En GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908812.2908935.
Texto completoXu, Yiheng, Qiangwei Wang y Jinglu Hu. "An Improved Discrete Particle Swarm Optimization Based on Cooperative Swarms". En 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.103.
Texto completoRao, Singiresu S. y Kiran K. Annamdas. "Particle Swarm Methodologies for Engineering Design Optimization". En ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87237.
Texto completoZhong Liu y Lei Huang. "A mixed discrete particle swarm optimization for TSP". En 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579238.
Texto completoInformes sobre el tema "Discrete Particle Swarm Optimization"
Vtipil, Sharon y John G. Warner. Earth Observing Satellite Orbit Design Via Particle Swarm Optimization. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2014. http://dx.doi.org/10.21236/ada625084.
Texto completoSonugür, Güray, Celal Onur Gçkçe, Yavuz Bahadır Koca y Şevket Semih Inci. Particle Swarm Optimization Based Optimal PID Controller for Quadcopters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, diciembre de 2021. http://dx.doi.org/10.7546/crabs.2021.12.11.
Texto completoGökçe, Barış, Yavuz Bahadır Koca, Yılmaz Aslan y Celal Onur Gökçe. Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, abril de 2021. http://dx.doi.org/10.7546/crabs.2021.04.12.
Texto completoDavis, Jeremy, Amy Bednar y Christopher Goodin. Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm. Engineer Research and Development Center (U.S.), septiembre de 2019. http://dx.doi.org/10.21079/11681/34160.
Texto completoStyling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, septiembre de 2021. http://dx.doi.org/10.4271/2021-01-5094.
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