Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization
En la aviación convencional, la planificación de las rutas de vuelo tiene por objeto garantizar la seguridad y reducir los costos de explotación de los diferentes tipos de aeronaves disponibles en la actualidad. En el presente documento se presentan y analizan los resultados obtenidos de la planific...
- Autores:
-
Colmenares Quintero, Ramón Fernando
Góez Sánchez, Germán David
Colmenares Quintero, Juan Carlos
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/17440
- Acceso en línea:
- https://doi.org/10.1080/23311916.2018.1429984
https://hdl.handle.net/20.500.12494/17440
- Palabra clave:
- Aiación comercial
Turbofan innovador
Planificación en tiempo real
Heuristic goal
Particle swarm optimization
Commercial aviation
Innovative turbofan
Real-time planning
- Rights
- openAccess
- License
- Atribución
| id |
COOPER2_0a0c295949b4ab44dbfefb8666175836 |
|---|---|
| oai_identifier_str |
oai:repository.ucc.edu.co:20.500.12494/17440 |
| network_acronym_str |
COOPER2 |
| network_name_str |
Repositorio UCC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| title |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| spellingShingle |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization Aiación comercial Turbofan innovador Planificación en tiempo real Heuristic goal Particle swarm optimization Commercial aviation Innovative turbofan Real-time planning |
| title_short |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| title_full |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| title_fullStr |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| title_full_unstemmed |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| title_sort |
Route planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimization |
| dc.creator.fl_str_mv |
Colmenares Quintero, Ramón Fernando Góez Sánchez, Germán David Colmenares Quintero, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Colmenares Quintero, Ramón Fernando Góez Sánchez, Germán David Colmenares Quintero, Juan Carlos |
| dc.subject.spa.fl_str_mv |
Aiación comercial Turbofan innovador Planificación en tiempo real |
| topic |
Aiación comercial Turbofan innovador Planificación en tiempo real Heuristic goal Particle swarm optimization Commercial aviation Innovative turbofan Real-time planning |
| dc.subject.other.spa.fl_str_mv |
Heuristic goal Particle swarm optimization Commercial aviation Innovative turbofan Real-time planning |
| description |
En la aviación convencional, la planificación de las rutas de vuelo tiene por objeto garantizar la seguridad y reducir los costos de explotación de los diferentes tipos de aeronaves disponibles en la actualidad. En el presente documento se presentan y analizan los resultados obtenidos de la planificación de una ruta de vuelo en tiempo real para aeronaves de corto alcance equipadas con turboventiladores de volumen constante para reducir los gastos de explotación mediante la optimización de la ruta en términos de menor distancia recorrida. En el análisis, una ruta fuera de línea obtenida a partir de un perfil de vuelo utilizando la herramienta del Marco de Diseño Multidisciplinario Preliminar se compara con la misma ruta que se recorre en tiempo real utilizando un planificador de rutas optimizado con un algoritmo de optimización de enjambre de partículas. Los resultados medios indican que la ruta propuesta por el algoritmo de optimización reduce los costos operativos directos e indirectos si se compara con la ruta alternativa (enfoque tradicional según el plan de vuelo). Esto se debe principalmente al menor consumo de combustible y a los menores costos de mantenimiento al seleccionar una ruta optimizada. En los casos en que las rutas directas y alternativas están completamente obstruidas, el planificador puede encontrar una ruta optimizada basándose en los costos más bajos. De esta manera, el piloto y el controlador de vuelo en tierra pueden tomar la decisión de arriesgar la aeronave con pasajeros en una ruta con condiciones meteorológicas adversas o de tener una optimizada y segura. El uso de este tipo de optimizador de rutas da lugar a importantes beneficios con respecto a los impuestos ambientales de las aeronaves para las emisiones y el ruido, y aumenta la seguridad de la aviación. Este hallazgo indica que el planificador de rutas es una opción de apoyo adecuada cuando el curso de una aeronave debe cambiarse para seguir una ruta diferente a la alternativa sugerida. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018-01-20 |
| dc.date.accessioned.none.fl_str_mv |
2020-04-21T19:45:04Z |
| dc.date.available.none.fl_str_mv |
2020-04-21T19:45:04Z |
| dc.type.none.fl_str_mv |
Artículo |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
publishedVersion |
| dc.identifier.issn.spa.fl_str_mv |
23311916 |
| dc.identifier.uri.spa.fl_str_mv |
https://doi.org/10.1080/23311916.2018.1429984 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12494/17440 |
| dc.identifier.bibliographicCitation.spa.fl_str_mv |
Ramón Fernando Colmenares-Quintero, Germán David Góez-Sánchez & Juan Carlos Colmenares-Quintero | Duc Pham (Reviewing Editor) (2018) Route planning in real time for short-range aircraft with a constant-volume-combustor-geared turbofan to minimize operating costs by particle swarm optimization, Cogent Engineering, 5:1, DOI: 10.1080/23311916.2018.1429984 |
| identifier_str_mv |
23311916 Ramón Fernando Colmenares-Quintero, Germán David Góez-Sánchez & Juan Carlos Colmenares-Quintero | Duc Pham (Reviewing Editor) (2018) Route planning in real time for short-range aircraft with a constant-volume-combustor-geared turbofan to minimize operating costs by particle swarm optimization, Cogent Engineering, 5:1, DOI: 10.1080/23311916.2018.1429984 |
| url |
https://doi.org/10.1080/23311916.2018.1429984 https://hdl.handle.net/20.500.12494/17440 |
| dc.relation.isversionof.spa.fl_str_mv |
https://www.tandfonline.com/doi/full/10.1080/23311916.2018.1429984?scroll=top&needAccess=true |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Cogent Engineering |
| dc.relation.references.spa.fl_str_mv |
Aeronáutica Civil Colombiana. (2015). Autoridad Aeronáutica. Retrieved from http://www.aerocivil.gov.co/AAeronautica/ Paginas/Inicio.aspx Anon. (2014). Reglas de vuelo visual, 4–5. AIP COLOMBIA ENR Clerc, M. (2005). Particle swarm optimization. Hermes Science/ Lavoisier Clerc, M. (2012). Standard particle swarm optimisation (Chapter 1, 1–19). Innovations and Developments of Swarm Intelligence Applications. Retrieved from https://pdfs. semanticscholar.org/c26f/ d373a085b72b9a83724ef44b0d3780f0b93e.pdf Colmenares, R. F., Brink, R., Ogaji, S., Pilidis, P., Singh, R., Colmenares, J. C., & García, A. (2009). Application 2009 power for land, sea and air conference, Orlando, USA, June 8–12 2009, GT2009-59096. Colmenares, R. F., Coutinho, A., Ogaji, S., Pilidis, P., Singh, R., Pincay, N. A., & Hernandez, J. C. (2009). Feasibility study of a conventional turbofan with a constant volume combustor. ASME Turbo Expo 2009 Power for Land, Air Conference, Orlando, USA, June 8–12 2009, GT2009-59097 Colmenares Quintero, R. F. (2009). Techno-economic and environmental risk assessment of innovative propulsion systems for short-range civil aircraft (PhD Thesis). Cranfield University, Cranfield, UK Colmenares Quintero, R. F., Brink, R., Ogaji, S., Pilidis, P., Colmenares Quintero, J. C., & Quintero, A. G. (2010). Application of the geared turbofan with constant volume combustor on short-range aircraft: A feasibility study. Journal of Engineering for Gas Turbines and Power, 132(6), 061702. doi:10.1115/1.4000135 Góez, G. D. (2016). Planeamiento de Rutas en Vehículos Aéreos No Tripulados usando Algoritmos Bio-inspirados sobre Sistemas (MSc Thesis). Instituto Tecnologico Metropolitano, Medellin, Colombia ICAO. (2009). Manual de diseño de procedimientos de performance de navegación requerida con autorización obligatoria (RNP AR) (O. de A. C. Internacional, Ed.). (2009th ed.). International Civil Aviation Organization. (2006). Convention on International Civil Aviation. Retrieved from http://www. icao.int/publications/documents/7300_9ed.pdf Jadoun, V. K., Gupta, N., Niazi, K. R., & Swarnkar, A. (2014). Nonconvex economic dispatch using particle swarm optimization with time varying operators. In The Advances in Electrical Engineering (Vol. 2014, 13 pages). Hindawi Publishing Corporation. Article ID 301615. doi:10.1155/2014/301615 Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015a, May). Short-term non-convex economic hydrothermal scheduling using dynamically controlled particle swarm optimization. 3rd Southern African Solar Energy Conference, South Africa, 11–13 May, 2015, 199– 204. Retrieved from http://doi.org/http://hdl.handle. net/2263/49490 Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015b). Improved particle swarm optimization for multiarea economic dispatch with reserve sharing scheme. IFAC-PapersOnLine, 48(30), 161–166. doi:10.1016/j.ifacol.2015.12.371 Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015c). Multi-area economic dispatch using improved particle swarm optimization. Energy Procedia, 75, 1087– 1092. doi:10.1016/j.egypro.2015.07.493 Jardin, M. (2003, June). Real-time conflict-free trajectory optimization. 5th USA/Europe ATM 2003 R&D Seminar. Retrieved from http://www.atmseminar.org/ seminarContent/seminar5/presentations/pr_027_TFO.pdf Jensen, L., Hansman, J. R., Venuti, J., & Reynolds, T. (2014). Commercial airline altitude optimization strategies for reduced cruise fuel consumption. 14th AIAA Aviation Technology, Integration, and Operations Conference (P. 3006). http://doi.org/10.2514 Lee, K.-B., & Kim, J.-H. (2013). Multiobjective particle swarm optimization with preference-based sort and its application to path following footstep optimization for humanoid robots. IEEE Transactions on Evolutionary Computation, 17(6), 755–766. doi:10.1109/TEVC.2013.2240688 Leonard, B. J., & Engelbrecht, A. P. (2013). On the optimality of particle swarm parameters in dynamic. Environments, 1564–1569. doi:10.1109/CEC.2013.6557748 Mason, K., & Miyoshi, C. (2009). Airline business models and their respective carbon footprint? Final Report Main Thematic Area? Economics Meng, H., & Xin, G. (2010). UAV route planning based on the genetic simulated annealing algorithm. IEEE International Conference on Mechatronics and Automation, 2010, 788– 793. doi:10.1109/ICMA.2010.5589035 Munoz, D. M., Llanos, C. H., Coelho, L. D. S., & Ayala-Rincon, M. (2010). Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications. In 2010 International Conference on Reconfigurable Computing and FPGAs, 55–60. doi:10.1109/ ReConFig. 2010.73 Murrieta-Mendoza, A., Botez, R. M., & Félix Patrón, R. S. (2015, September). Flight altitude optimization using genetic algorithms considering climb and descent costs in cruise with flight plan information (SAE Technical Papers). doi:10.4271/2015-01-254 Peng, Z., & Li, B. (2012). Online route planning for UAV based on model predictive control and particle swarm optimization algorithm, (60925011), 397–401. doi:10.1109/WCICA.2012.6357907 Roberge, V., Tarbouchi, M., & Labonté, G. (2013). Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics, 9(1), 132–141. Tewolde, G. S., Hanna, D. M., & Haskell, R. E. (2009). Accelerating the performance of particle swarm optimization for embedded applications. In 2009 IEEE Congress on Evolutionary Computation, 2294–2300. doi:10.1109/CEC.2009.4983226 Wang, Y., Yin, H., Zhang, S., & Yu, X. (2014). Multi-objective optimization of aircraft design for emission and cost reductions. Chinese Journal of Aeronautics, 27(1), 52–58. doi:10.1016/j.cja.2013.12.008. Xu, J., Andrew Ning, S., Bower, G., & Kroo, I. (2014). Aircraft route optimization for formation flight. Journal of Aircraft, 51(2), 490–501. doi:10.2514/1.C032154 Xue, Q., Cheng, P., & Cheng, N. (2014). Offline path planning and online replanning of UAVs in complex terrain, 2287– 2292. doi:10.1109/CGNCC.2014.7007525 Yang, X., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing and Applications, 24, 169–174. doi:10.1109/CGNCC.2014.7007525 Yang, X., & Press, L. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Luniver Press Frome, BA11 6TT, United Kingdom. Retrieved from www.luniver.com |
| dc.rights.license.none.fl_str_mv |
Atribución |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Atribución http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
1-16 p. |
| dc.coverage.temporal.spa.fl_str_mv |
5 |
| dc.publisher.spa.fl_str_mv |
Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y Envigado |
| dc.publisher.program.spa.fl_str_mv |
Ingeniería Civil |
| dc.publisher.place.spa.fl_str_mv |
Medellín |
| institution |
Universidad Cooperativa de Colombia |
| bitstream.url.fl_str_mv |
https://repository.ucc.edu.co/bitstreams/9afbfcae-2ae0-4cdb-a175-bc0ce8b1e26a/download https://repository.ucc.edu.co/bitstreams/82bfa82e-728b-41b0-8ba5-d1606bd4658f/download https://repository.ucc.edu.co/bitstreams/c1c02c3d-c3db-4b55-9494-617b381cd04a/download https://repository.ucc.edu.co/bitstreams/e3dea8a6-f0c0-438d-b89f-cbc2814d4ae3/download https://repository.ucc.edu.co/bitstreams/f61d614c-b275-47ce-a69e-83712c92d0e8/download https://repository.ucc.edu.co/bitstreams/01b35293-1d06-47f0-9ccf-c76beba74c4b/download https://repository.ucc.edu.co/bitstreams/39e28d54-fd06-4b39-9cbb-d4b60c39f559/download |
| bitstream.checksum.fl_str_mv |
f2680202e522ba75cd1bd00b384c9655 21b622474bf7677ba6adab9f154f60c6 3bce4f7ab09dfc588f126e1e36e98a45 91bf9aa1675e4435427112af0efa3c4e f278df7ec3464c518d49c6d559087a74 3ec98730f1f22197e8bd5723ef31e362 a4468dda5021583fa0c6042c3582e0b6 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Cooperativa de Colombia |
| repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
| _version_ |
1808789362319556608 |
| spelling |
Colmenares Quintero, Ramón FernandoGóez Sánchez, Germán DavidColmenares Quintero, Juan Carlos52020-04-21T19:45:04Z2020-04-21T19:45:04Z2018-01-2023311916https://doi.org/10.1080/23311916.2018.1429984https://hdl.handle.net/20.500.12494/17440Ramón Fernando Colmenares-Quintero, Germán David Góez-Sánchez & Juan Carlos Colmenares-Quintero | Duc Pham (Reviewing Editor) (2018) Route planning in real time for short-range aircraft with a constant-volume-combustor-geared turbofan to minimize operating costs by particle swarm optimization, Cogent Engineering, 5:1, DOI: 10.1080/23311916.2018.1429984En la aviación convencional, la planificación de las rutas de vuelo tiene por objeto garantizar la seguridad y reducir los costos de explotación de los diferentes tipos de aeronaves disponibles en la actualidad. En el presente documento se presentan y analizan los resultados obtenidos de la planificación de una ruta de vuelo en tiempo real para aeronaves de corto alcance equipadas con turboventiladores de volumen constante para reducir los gastos de explotación mediante la optimización de la ruta en términos de menor distancia recorrida. En el análisis, una ruta fuera de línea obtenida a partir de un perfil de vuelo utilizando la herramienta del Marco de Diseño Multidisciplinario Preliminar se compara con la misma ruta que se recorre en tiempo real utilizando un planificador de rutas optimizado con un algoritmo de optimización de enjambre de partículas. Los resultados medios indican que la ruta propuesta por el algoritmo de optimización reduce los costos operativos directos e indirectos si se compara con la ruta alternativa (enfoque tradicional según el plan de vuelo). Esto se debe principalmente al menor consumo de combustible y a los menores costos de mantenimiento al seleccionar una ruta optimizada. En los casos en que las rutas directas y alternativas están completamente obstruidas, el planificador puede encontrar una ruta optimizada basándose en los costos más bajos. De esta manera, el piloto y el controlador de vuelo en tierra pueden tomar la decisión de arriesgar la aeronave con pasajeros en una ruta con condiciones meteorológicas adversas o de tener una optimizada y segura. El uso de este tipo de optimizador de rutas da lugar a importantes beneficios con respecto a los impuestos ambientales de las aeronaves para las emisiones y el ruido, y aumenta la seguridad de la aviación. Este hallazgo indica que el planificador de rutas es una opción de apoyo adecuada cuando el curso de una aeronave debe cambiarse para seguir una ruta diferente a la alternativa sugerida.In conventional aviation, flight route planning aims to guarantee security and reduce the operating costs of the different types of aircraft that are currently available. This paper presents and analyses the results obtained from planning a flight route in real time for short-range aircraft fitted with geared turbofans with a constant volume combustor to reduce operating costs by optimizing the route in terms of less distance traveled. In the analysis, an off-line route obtained from a flight profile using the Preliminary Multidisciplinary Design Framework tool is compared with the same route that is run in real time using a route planner optimized with a particle swarm optimization algorithm. The average results indicate that the route proposed by the optimization algorithm reduces direct and indirect operating costs if compared to the alternative route (traditional approach according to flight plan). This is mainly due to the lower fuel consumption and maintenance costs when selecting an optimized route. In cases where direct and alternate routes are completely obstructed, the planner can find an optimized route based on the lower costs. In this way, the pilot and the flight controller on the ground can make the decision whether to risk the aircraft with passengers on a route with adverse weather conditions or to have an optimized and safe one. The use of this type of route optimizer leads to significant benefits with respect to aircraft environmental taxes for emissions and noise, and increases aviation safety. This finding indicates that the route planner is a suitable support option when the course of an aircraft must be changed to follow a different route than the suggested alternative.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001358296#articuloshttps://orcid.org/0000-0003-1166-1982https://sba.minciencias.gov.co/Buscador_Grupos/busqueda?q=TERMOMEC&pagenum=1&start=0&type=load&inmeta=COD_ID_GRUPO!COL0054239ramon.colmenaresq@campusucc.edu.cojcarloscolmenares@ichf.edu.plhttps://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=es1-16 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y EnvigadoIngeniería CivilMedellínhttps://www.tandfonline.com/doi/full/10.1080/23311916.2018.1429984?scroll=top&needAccess=trueCogent EngineeringAeronáutica Civil Colombiana. (2015). Autoridad Aeronáutica. Retrieved from http://www.aerocivil.gov.co/AAeronautica/ Paginas/Inicio.aspxAnon. (2014). Reglas de vuelo visual, 4–5. AIP COLOMBIA ENRClerc, M. (2005). Particle swarm optimization. Hermes Science/ LavoisierClerc, M. (2012). Standard particle swarm optimisation (Chapter 1, 1–19). Innovations and Developments of Swarm Intelligence Applications. Retrieved from https://pdfs. semanticscholar.org/c26f/ d373a085b72b9a83724ef44b0d3780f0b93e.pdfColmenares, R. F., Brink, R., Ogaji, S., Pilidis, P., Singh, R., Colmenares, J. C., & García, A. (2009). Application 2009 power for land, sea and air conference, Orlando, USA, June 8–12 2009, GT2009-59096.Colmenares, R. F., Coutinho, A., Ogaji, S., Pilidis, P., Singh, R., Pincay, N. A., & Hernandez, J. C. (2009). Feasibility study of a conventional turbofan with a constant volume combustor. ASME Turbo Expo 2009 Power for Land, Air Conference, Orlando, USA, June 8–12 2009, GT2009-59097Colmenares Quintero, R. F. (2009). Techno-economic and environmental risk assessment of innovative propulsion systems for short-range civil aircraft (PhD Thesis). Cranfield University, Cranfield, UKColmenares Quintero, R. F., Brink, R., Ogaji, S., Pilidis, P., Colmenares Quintero, J. C., & Quintero, A. G. (2010). Application of the geared turbofan with constant volume combustor on short-range aircraft: A feasibility study. Journal of Engineering for Gas Turbines and Power, 132(6), 061702. doi:10.1115/1.4000135Góez, G. D. (2016). Planeamiento de Rutas en Vehículos Aéreos No Tripulados usando Algoritmos Bio-inspirados sobre Sistemas (MSc Thesis). Instituto Tecnologico Metropolitano, Medellin, ColombiaICAO. (2009). Manual de diseño de procedimientos de performance de navegación requerida con autorización obligatoria (RNP AR) (O. de A. C. Internacional, Ed.). (2009th ed.).International Civil Aviation Organization. (2006). Convention on International Civil Aviation. Retrieved from http://www. icao.int/publications/documents/7300_9ed.pdfJadoun, V. K., Gupta, N., Niazi, K. R., & Swarnkar, A. (2014). Nonconvex economic dispatch using particle swarm optimization with time varying operators. In The Advances in Electrical Engineering (Vol. 2014, 13 pages). Hindawi Publishing Corporation. Article ID 301615. doi:10.1155/2014/301615Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015a, May). Short-term non-convex economic hydrothermal scheduling using dynamically controlled particle swarm optimization. 3rd Southern African Solar Energy Conference, South Africa, 11–13 May, 2015, 199– 204. Retrieved from http://doi.org/http://hdl.handle. net/2263/49490Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015b). Improved particle swarm optimization for multiarea economic dispatch with reserve sharing scheme. IFAC-PapersOnLine, 48(30), 161–166. doi:10.1016/j.ifacol.2015.12.371Jadoun, V. K., Gupta, N., Niazi, K. R., Swarnkar, A., & Bansal, R. C. (2015c). Multi-area economic dispatch using improved particle swarm optimization. Energy Procedia, 75, 1087– 1092. doi:10.1016/j.egypro.2015.07.493Jardin, M. (2003, June). Real-time conflict-free trajectory optimization. 5th USA/Europe ATM 2003 R&D Seminar. Retrieved from http://www.atmseminar.org/ seminarContent/seminar5/presentations/pr_027_TFO.pdfJensen, L., Hansman, J. R., Venuti, J., & Reynolds, T. (2014). Commercial airline altitude optimization strategies for reduced cruise fuel consumption. 14th AIAA Aviation Technology, Integration, and Operations Conference (P. 3006). http://doi.org/10.2514Lee, K.-B., & Kim, J.-H. (2013). Multiobjective particle swarm optimization with preference-based sort and its application to path following footstep optimization for humanoid robots. IEEE Transactions on Evolutionary Computation, 17(6), 755–766. doi:10.1109/TEVC.2013.2240688Leonard, B. J., & Engelbrecht, A. P. (2013). On the optimality of particle swarm parameters in dynamic. Environments, 1564–1569. doi:10.1109/CEC.2013.6557748Mason, K., & Miyoshi, C. (2009). Airline business models and their respective carbon footprint? Final Report Main Thematic Area? EconomicsMeng, H., & Xin, G. (2010). UAV route planning based on the genetic simulated annealing algorithm. IEEE International Conference on Mechatronics and Automation, 2010, 788– 793. doi:10.1109/ICMA.2010.5589035Munoz, D. M., Llanos, C. H., Coelho, L. D. S., & Ayala-Rincon, M. (2010). Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications. In 2010 International Conference on Reconfigurable Computing and FPGAs, 55–60. doi:10.1109/ ReConFig. 2010.73Murrieta-Mendoza, A., Botez, R. M., & Félix Patrón, R. S. (2015, September). Flight altitude optimization using genetic algorithms considering climb and descent costs in cruise with flight plan information (SAE Technical Papers). doi:10.4271/2015-01-254Peng, Z., & Li, B. (2012). Online route planning for UAV based on model predictive control and particle swarm optimization algorithm, (60925011), 397–401. doi:10.1109/WCICA.2012.6357907Roberge, V., Tarbouchi, M., & Labonté, G. (2013). Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics, 9(1), 132–141.Tewolde, G. S., Hanna, D. M., & Haskell, R. E. (2009). Accelerating the performance of particle swarm optimization for embedded applications. In 2009 IEEE Congress on Evolutionary Computation, 2294–2300. doi:10.1109/CEC.2009.4983226Wang, Y., Yin, H., Zhang, S., & Yu, X. (2014). Multi-objective optimization of aircraft design for emission and cost reductions. Chinese Journal of Aeronautics, 27(1), 52–58. doi:10.1016/j.cja.2013.12.008.Xu, J., Andrew Ning, S., Bower, G., & Kroo, I. (2014). Aircraft route optimization for formation flight. Journal of Aircraft, 51(2), 490–501. doi:10.2514/1.C032154Xue, Q., Cheng, P., & Cheng, N. (2014). Offline path planning and online replanning of UAVs in complex terrain, 2287– 2292. doi:10.1109/CGNCC.2014.7007525Yang, X., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing and Applications, 24, 169–174. doi:10.1109/CGNCC.2014.7007525Yang, X., & Press, L. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Luniver Press Frome, BA11 6TT, United Kingdom. Retrieved from www.luniver.comAiación comercialTurbofan innovadorPlanificación en tiempo realHeuristic goalParticle swarm optimizationCommercial aviationInnovative turbofanReal-time planningRoute planning in real time for short-range aircraft with a constant-volume-combustorgeared turbofan to minimize operating costs by particle swarm optimizationArtículohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribucióninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINALRoute_planning_aircraft_licenciadeuso.pdfRoute_planning_aircraft_licenciadeuso.pdfLicencia de Usoapplication/pdf170050https://repository.ucc.edu.co/bitstreams/9afbfcae-2ae0-4cdb-a175-bc0ce8b1e26a/downloadf2680202e522ba75cd1bd00b384c9655MD52Route_planning_aircraft.pdfRoute_planning_aircraft.pdfArticuloapplication/pdf1939251https://repository.ucc.edu.co/bitstreams/82bfa82e-728b-41b0-8ba5-d1606bd4658f/download21b622474bf7677ba6adab9f154f60c6MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84334https://repository.ucc.edu.co/bitstreams/c1c02c3d-c3db-4b55-9494-617b381cd04a/download3bce4f7ab09dfc588f126e1e36e98a45MD54THUMBNAILRoute_planning_aircraft_licenciadeuso.pdf.jpgRoute_planning_aircraft_licenciadeuso.pdf.jpgGenerated Thumbnailimage/jpeg5090https://repository.ucc.edu.co/bitstreams/e3dea8a6-f0c0-438d-b89f-cbc2814d4ae3/download91bf9aa1675e4435427112af0efa3c4eMD55Route_planning_aircraft.pdf.jpgRoute_planning_aircraft.pdf.jpgGenerated Thumbnailimage/jpeg4609https://repository.ucc.edu.co/bitstreams/f61d614c-b275-47ce-a69e-83712c92d0e8/downloadf278df7ec3464c518d49c6d559087a74MD56TEXTRoute_planning_aircraft_licenciadeuso.pdf.txtRoute_planning_aircraft_licenciadeuso.pdf.txtExtracted texttext/plain5640https://repository.ucc.edu.co/bitstreams/01b35293-1d06-47f0-9ccf-c76beba74c4b/download3ec98730f1f22197e8bd5723ef31e362MD57Route_planning_aircraft.pdf.txtRoute_planning_aircraft.pdf.txtExtracted texttext/plain49801https://repository.ucc.edu.co/bitstreams/39e28d54-fd06-4b39-9cbb-d4b60c39f559/downloada4468dda5021583fa0c6042c3582e0b6MD5820.500.12494/17440oai:repository.ucc.edu.co:20.500.12494/174402024-08-10 21:02:24.59restrictedhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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 |