Academic literature on the topic 'Algoritmus PSO'
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Journal articles on the topic "Algoritmus PSO"
Tang, Zhonghua, and Yongquan Zhou. "A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning." Journal of Intelligent Systems 24, no. 1 (March 1, 2015): 69–83. http://dx.doi.org/10.1515/jisys-2013-0066.
Full textJovanović, Dražen, Martin Ćalasan, and Milovan Radulović. "Estimation of solar cell parameters using PSO algorithm." Tehnika 74, no. 1 (2019): 91–96. http://dx.doi.org/10.5937/tehnika1901091j.
Full textMujičić, Danilo, Martin Ćalasan, and Milovan Radulović. "Application of PSO algorithm in transformer parameter estimation." Tehnika 74, no. 2 (2019): 251–57. http://dx.doi.org/10.5937/tehnika1902251m.
Full textLemonge, Afonso Celso de Castro, Patrícia Habib Hallak, and José Pedro Gonçalves Carvalho. "Otimização estrutural de treliças considerando restrições de frequências naturais de vibração." Principia: Caminhos da Iniciação Científica 18, no. 2 (March 5, 2020): 14. http://dx.doi.org/10.34019/2179-3700.2018.v18.29874.
Full textQuah, Titus, Derek Machalek, and Kody M. Powell. "Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process." Processes 8, no. 11 (November 19, 2020): 1497. http://dx.doi.org/10.3390/pr8111497.
Full textObando Paredes, Edgar Dario. "Algoritmos genéticos y PSO aplicados a un problema de generación distribuida." Scientia et technica 22, no. 1 (March 30, 2017): 15. http://dx.doi.org/10.22517/23447214.14301.
Full textVojtíšek, Michal, and Martin Kotek. "Estimation of Engine Intake Air Mass Flow using a generic Speed-Density method." Journal of Middle European Construction and Design of Cars 12, no. 1 (October 1, 2014): 7–15. http://dx.doi.org/10.2478/mecdc-2014-0002.
Full textLenin, K. "POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (June 30, 2018): 335–45. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1378.
Full textChu, Shu-Chuan, Zhi-Gang Du, and Jeng-Shyang Pan. "Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks." Applied Sciences 10, no. 3 (January 31, 2020): 930. http://dx.doi.org/10.3390/app10030930.
Full textCheng, Shi, Yuhui Shi, and Quande Qin. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization." International Journal of Swarm Intelligence Research 2, no. 3 (July 2011): 43–69. http://dx.doi.org/10.4018/jsir.2011070104.
Full textDissertations / Theses on the topic "Algoritmus PSO"
Ondráček, Pavel. "Optimalizační techniky v obrazových aplikacích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442498.
Full textGötthans, Tomáš. "Pokročilé algoritmy analýzy datových sekvencí v Matlabu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218618.
Full textKolářová, Jana. "Evoluční algoritmy pro ultrazvukovou perfúzní analýzu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401037.
Full textRusnák, Jakub. "Vizualizace algoritmů pro plánování cesty." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363819.
Full textHulva, Jiří. "Koevoluční algoritmus pro úlohy založené na testu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236019.
Full textVolf, Lukáš. "Program pro testování rychlosti šifrovacích algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218674.
Full textBodeček, Miroslav. "Algoritmus pro cílené doporučování produktů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-412860.
Full textPovalač, Karel. "Sledování spektra a optimalizace systémů s více nosnými pro kognitivní rádio." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-233577.
Full textMelo, Leonardo Alves Moreira de. "Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/9108.
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Made available in DSpace on 2018-11-29T11:09:58Z (GMT). No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-10-26
Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state- of-the-art PSO algorithms (CCPSO2, LSS- PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the e ciency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble realworld problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL- PSO seem to be highly competitive optimization algorithms to solve complex and multimodal optimization problems.
O número de algoritmos baseados na otimização por enxame de partículas (PSO) aplicados para resolver problemas de otimização em grande escala (até 2.000 variáveis) aumentou significativamente. Este trabalho apresenta análises e comparações entre cinco algoritmos (CCPSO2, LSSPSO, OBL-CPSO, SPSO e VCPSO). Testes foram realizados para ilustrar a eficiência e viabilidade de usar os algoritmos para resolver problemas em larga escala. Seis funções de referência que são comumente utilizadas na literatura (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 e Sphere) foram utilizadas para testar a performancedesses algoritmos. Os experimentos foram realizados utilizando um problema de alta dimensionalidade (500 variáveis), variando o número de partículas (50, 100 e 200 partículas) em cada algoritmo, aumentando assim a complexidade computacional. A análise mostrou que os algoritmos CCPSO2 e OBL-CPSO mostraram-se significativamente melhores que os outros algoritmos para problemas multimodais mais complexos (que mais se assemelham a problemas reais). No entanto, considerando as funções unimodais, o algoritmo CCPSO2 destacou-se perante os demais. Nossos resultados e análises experimentais sugerem que o CCPSO2 e o OBL-CPSO são algoritmos de otimização altamente competitivos para resolver problemas de otimização complexos e multimodais em larga escala.
Horáková, Pavla. "Optimalizace pro registraci obrazů založená na genetických algoritmech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219505.
Full textBooks on the topic "Algoritmus PSO"
Ayyadevara, V. Kishore. Pro Machine Learning Algorithms. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5.
Full textVišňák, Jakub. Kvantově chemické algoritmy pro kvantové počítače. Praha: Academia, 2014.
Find full textŠtuller, Július. Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu: Seminář projektu programu "Informační společnost", 5.10.-7.10.2006, hotel Olympia, Zadov. Praha: Ústav informatiky AV ČR, 2006.
Find full textAyyadevara, V. Kishore. Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R. Apress, 2018.
Find full textLópez, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.
Full textCoates, Laura C., Arthur Kavanaugh, and Christopher T. Ritchlin. Treatment algorithm and treat to target. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0032.
Full textPro .NET Performance: Optimize Your C# Applications. Apress, 2012.
Find full textProgramming Like A Pro For Teens. Course Technology, 2011.
Find full text(Editor), Dorit Hochbaum, Klaus Jansen (Editor), Jose D.P. Rolim (Editor), and Alistair Sinclair (Editor), eds. Randomization, Approximation, and Combinatorial Optimization. Algorithms and Techniques: Third International Workshop on Randomization and Approximation ... 1999 Pro (Lecture Notes in Computer Science). Springer, 1999.
Find full textRosales Torres, César Said, César Buenadicha Sánchez, and Tetsuro Narita. Autoevaluación ética de IA para actores del ecosistema emprendedor: Guía de aplicación. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003269.
Full textBook chapters on the topic "Algoritmus PSO"
Krzeszowski, Tomasz, Krzysztof Wiktorowicz, and Krzysztof Przednowek. "Comparison of Selected Fuzzy PSO Algorithms." In Recent Advances in Computational Optimization, 107–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59861-1_7.
Full textVoss, Michael, Rafael Asenjo, and James Reinders. "Generic Parallel Algorithms." In Pro TBB, 33–78. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4398-5_2.
Full textSaremi, Shahrzad, and Seyedali Mirjalili. "Evaluating PSO and MOPSO Equipped with Evolutionary Population Dynamics." In Algorithms for Intelligent Systems, 61–88. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9757-8_4.
Full textKumar, Manish, Manish Gupta, Divesh Kumar, and Vinay kumar Deolia. "Optimization of Low Power LNA Using PSO for UWB Application." In Algorithms for Intelligent Systems, 393–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_37.
Full textYamada, Shinichi, and Kourosh Neshatian. "Improving Representation in Benchmarking of Binary PSO Algorithms." In AI 2018: Advances in Artificial Intelligence, 371–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_35.
Full textAbdulla, Parosh Aziz, Stavros Aronis, Mohamed Faouzi Atig, Bengt Jonsson, Carl Leonardsson, and Konstantinos Sagonas. "Stateless Model Checking for TSO and PSO." In Tools and Algorithms for the Construction and Analysis of Systems, 353–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46681-0_28.
Full textDin, Maiya, Saibal K. Pal, and S. K. Muttoo. "Analysis of RC4 Crypts Using PSO Based Swarm Technique." In Harmony Search and Nature Inspired Optimization Algorithms, 1049–56. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_98.
Full textVoss, Michael, Rafael Asenjo, and James Reinders. "Using Tasks to Create Your Own Algorithms." In Pro TBB, 277–311. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4398-5_10.
Full textSuthar, Jaswant, Virendra Sangtani, and Ajay Kumar Bansal. "Optimal Design of a Stand-Alone Hybrid System Using GA, PSO and ABC." In Algorithms for Intelligent Systems, 179–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1059-5_21.
Full textAhmad, Salman, and Atif Iqbal. "Switching Angles Computations Using PSO in Selective Harmonics Minimization PWM." In Metaheuristic and Evolutionary Computation: Algorithms and Applications, 437–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7571-6_18.
Full textConference papers on the topic "Algoritmus PSO"
Nguyen, Thanh-Trung, Van-Huy Nguyen, and Xuan-Huong Nguyen. "Comparing the Results of Applying DE, PSO and Proposed Pro DE, Pro PSO Algorithms for Inverse Kinematics Problem of a 5-DOF Scara Robot." In 2020 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2020. http://dx.doi.org/10.1109/icamechs49982.2020.9310157.
Full textSyed Abdullah, Sharifah Lailee, Naimah Mohd Hussin, Hazaruddin Harun, and Noor Elaiza Abd Khalid. "Comparative study of random-PSO and Linear-PSO algorithms." In 2012 International Conference on Computer & Information Science (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccisci.2012.6297280.
Full textAnescu, George, and Ilie Prisecaru. "NSC-PSO, a Novel PSO Variant without Speeds and Coefficients." In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2015. http://dx.doi.org/10.1109/synasc.2015.74.
Full textM. R. Baccarini, João, Francisco C. R. Coelho, Lane M. Rabelo, and Gleison F. V. Amaral. "Estimação Online dos Parâmetros do Circuito Equivalente do Motor de Indução Trifásico via Metaheurísticas." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1408.
Full textYoder, Marcus, Zachary Satterfield, Mohammad Fazelpour, Joshua D. Summers, and Georges Fadel. "Numerical Methods for the Design of Meso-Structures: A Comparative Review." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46289.
Full textBreaban, Mihaela, and Silvia Luchian. "Shaping Up Clusters with PSO." In 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2008. http://dx.doi.org/10.1109/synasc.2008.70.
Full textJiang, Weichang, Yating Zhang, and Ruihua Wang. "Comparative study on several PSO algorithms." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852332.
Full textBabaee, H., and A. Khosravi. "An Improve PSO Based Hybrid Algorithms." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5999410.
Full textArakawa, Masao. "PSO Driven Genetic Range Genetic Algorithms." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84942.
Full textRocha, Lídia, and Kelen Vivaldini. "Comparison between Meta-Heuristic Algorithms for Path Planning." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14950.
Full textReports on the topic "Algoritmus PSO"
Labunskaya, A. D., B. A. Mukhin, P. G. Kolyadin, V. A. Kosyrev, E. A. Chepurnaya, and A. L. Shevchenko. The model of optimal allocation of resources of the warring parties on the basis of genetic algorithms (MOPP PS). OFERNIO, March 2020. http://dx.doi.org/10.12731/ofernio.2020.24495.
Full textElacqua, Gregory, Isabel Jacas, Thomas Krussig, Carolina Méndez, Christopher Neilson, Alonso Román, and Sammara Soares. Sistemas centralizados de asignación escolar: guía de implementación. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003362.
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