nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

11. Different Applications of PSO

verfasst von : Altaf Q. H. Badar

Erschienen in: Applying Particle Swarm Optimization

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Particle swarm optimization (PSO) is an evolutionary optimization algorithm. PSO is a robust and well researched optimization technique. There are a large number of applications of PSO. “Applications of PSO” chapter tries to present a classified literature review for the applications of PSO in different fields. The applications are classified into different sections based on the area of implementation.

The chapter also presents a table with references to multiple other applications over and above those covered in the chapter. References of some largely cited review papers dealing with the applications of PSO are also mentioned at the end of the chapter.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization

Nächstes Kapitel Particle Swarm Optimization in Global Path Planning for Swarm of Robots

Adewumi, A. O., & Popoola, P. A. (2018). A multi-objective particle swarm optimization for the submission decision process. International Journal of Systems Assurance Engineering and Management, 9(1), 98–110.CrossRef

Ahmadi, A., Alinejad-Beromi, Y., & Moradi, M. (2011). Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Expert Systems with Applications, 38(6), 7263–7269.CrossRef

Ai, T. J., & Kachitvichyanukul, V. (2009). A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693–1702.CrossRef

Al Bahrani, L. T., & Patra, J. C. (2017). Orthogonal pso algorithm for economic dispatch of thermal generating units under various power constraints in smart power grid. Applied Soft Computing, 58, 401–426.CrossRef

AlRashidi, M. R., & El-Hawary, M. E. (2008). A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation, 13(4), 913–918.CrossRef

Anvari, M., Mehrabad, M. S., & Barzinpour, F. (2010). Machine–part cell formation using a hybrid particle swarm optimization. The International Journal of Advanced Manufacturing Technology, 47(5-8), 745–754.CrossRef

Armano, G., & Farmani, M. R. (2016). Multiobjective clustering analysis using particle swarm optimization. Expert Systems with Applications, 55, 184–193.CrossRef

Awad, A., El-Hefnawy, N., & Abdel Kader, H. (2015). Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, 920–929.CrossRef

Badar, A. Q., Umre, B., & Junghare, A. (2012). Reactive power control using dynamic particle swarm optimization for real power loss minimization. International Journal of Electrical Power & Energy Systems, 41(1), 133–136.CrossRef

Bahadur, I. M., & Mills, J. K. (2013). Robust autofocusing in microscopy using particle swarm optimization. In 2013 IEEE International Conference on Mechatronics and Automation (pp. 213–218). New York: IEEE.CrossRef

Banks, A., Vincent, J., & Anyakoha, C. (2008). A review of particle swarm optimization. Part II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing, 7(1), 109–124.CrossRef

Bensingh, R. J., Machavaram, R., Boopathy, S. R., & Jebaraj, C. (2019). Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (anns) and particle swarm optimization (pso). Measurement, 134, 359–374.CrossRef

Chen, W.-J., Su, W.-C., Nian, F.-L., Lin, J.-R., & Chen, D.-C. (2013). Application of anova and taguchi-based mutation particle swarm algorithm for parameters design of multi-hole extrusion process. Research Journal of Applied Sciences, Engineering and Technology, 6(13), 2316–2325.CrossRef

Connolly, J. F., Granger, E., & Sabourin, R. (2012). Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition. Pattern Recognition, 45(7), 2460–2477.CrossRef

Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In Advances of computational intelligence in industrial systems (pp. 1–38). Berlin: Springer.

Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J.-C., & Harley, R. G. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12(2), 171–195.CrossRef

Elsheikh, A., & Elaziz, M. A. (2019). Review on applications of particle swarm optimization in solar energy systems. International journal of Environmental Science and Technology, 16(2), 1159–1170.CrossRef

Esmin, A. A., Coelho, R. A., & Matwin, S. (2015). A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artificial Intelligence Review, 44(1), 23–45.CrossRef

Gupta, D. K., Reddy, K. S., Ekbal, A., et al. (2015). Pso-asent: Feature selection using particle swarm optimization for aspect based sentiment analysis. In International conference on applications of natural language to information systems (pp. 220–233). Saarbrücken, Germany: NLDB.

Hajihassani, M., Armaghani, D. J., & Kalatehjari, R. (2018). Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotechnical and Geological Engineering, 36(2), 705–722.CrossRef

Hereford, J. M. (2006). A distributed particle swarm optimization algorithm for swarm robotic applications. In 2006 IEEE International conference on evolutionary computation (pp. 1678–1685). New York: IEEE.CrossRef

Hochmuth, C. A., Lassig, J., & Thiem, S. (2011). Optimizing complex multilocation inventory models using particle swarm optimization. In Computational optimization, methods and algorithms (pp. 101–124). Berlin: Springer.CrossRef

Hosseini, S. H., Kashtiban, A. M., & Alizadeh, G. (2006). Particle swarm optimization and finite-element based approach for induction heating cooker design. In 2006 SICE-ICASE International joint conference (pp. 4624–4627). Busan: IEEE.CrossRef

Hu, W., Yan, L., Liu, K., & Wang, H. (2016). A short-term traffic flow forecasting method based on the hybrid pso-svr. Neural Processing Letters, 43(1), 155–172.CrossRef

Huang, C.-M., Huang, C.-J., & Wang, M.-L. (2005). A particle swarm optimization to identifying the armax model for short-term load forecasting. IEEE Transactions on Power Systems, 20(2), 1126–1133.CrossRef

Ishaque, K., Salam, Z., Amjad, M., & Mekhilef, S. (2012). An improved particle swarm optimization (pso)–based mppt for pv with reduced steady-state oscillation. IEEE Transactions on Power Electronics, 27(8), 3627–3638.CrossRef

Kamaraj, N. (2011). Transmission congestion management using particle swarm optimization. Journal of Electrical Systems, 7(1), 54–70.

Khan, A., Laha, S., & Sarkar, S. K. (2013). A novel particle swarm optimization approach for VLSI routing. In 2013 3rd IEEE international advance computing conference (IACC) (pp. 258–262). Ghaziabad: IEEE.CrossRef

Krusienski, D. J., & Jenkins, W. K. (2004). Particle swarm optimization for adaptive IIR filter structures. In Proceedings of the 2004 congress on evolutionary computation (IEEE cat. no. 04th8753) (Vol. 1, pp. 965–970). New York: IEEE.CrossRef

Kulkarni, M. N. K., Patekar, M. S., Bhoskar, M. T., Kulkarni, M. O., Kakandikar, G., & Nandedkar, V. (2015). Particle swarm optimization applications to mechanical engineering-a review. Materials Today: Proceedings, 2(4-5), 2631–2639.

Kuo, R. J., Chao, C. M., & Chiu, Y. (2011). Application of particle swarm optimization to association rule mining. Applied Soft Computing, 11(1), 326–336.CrossRef

Liu, C., Yan, C., & Wang, J. (2014). Hybrid particle swarm optimization algorithm and its application in nuclear engineering. Annals of Nuclear Energy, 64, 276–286.CrossRef

Onwunalu, J. E., & Durlofsky, L. J. (2010). Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 14(1), 183–198.CrossRef

Pare, V., Agnihotri, G., & Krishna, C. (2011). Optimization of cutting conditions in end milling process with the approach of particle swarm optimization. International Journal of Mechanical and Industrial Engineering, 1(2), 21–25.

Parsopoulos, K. E., & Vrahatis, M. N. (2010). Particle swarm optimization and intelligence: advances and applications.

Pedrasa, M. A. A., Spooner, T. D., & MacGill, I. F. (2010). Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Transactions on Smart Grid, 1(2), 134–143.CrossRef

Petalas, Y. G., Parsopoulos, K. E., & Vrahatis, M. N. (2009). Improving fuzzy cognitive maps learning through memetic particle swarm optimization. Soft Computing, 13(1), 77.CrossRef

Phommixay, S., Doumbia, M. L., & St-Pierre, D. L. (2020). Review on the cost optimization of microgrids via particle swarm optimization. International Journal of Energy and Environmental Engineering, 11(1), 73–89.CrossRef

Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T., & Zelinka, I. (2017). A review of real-world applications of particle swarm optimization algorithm. In International conference on advanced engineering theory and applications (pp. 115–122). Cham: Springer.

Poli, R. (2008). Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications, 2008, 685175.CrossRef

Prata, D. M., Schwaab, M., Lima, E. L., & Pinto, J. C. (2010). Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor. Chemical Engineering Science, 65(17), 4943–4954.CrossRef

Rana, S., Jasola, S., & Kumar, R. (2011). A review on particle swarm optimization algorithms and their applications to data clustering. Artificial Intelligence Review, 35(3), 211–222.CrossRef

Robinson, J., & Rahmat-Samii, Y. (2004). Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation, 52(2), 397–407.CrossRef

Samal, N. K., & Pratihar, D. K. (2015). Joint optimization of preventive maintenance and spare parts inventory using genetic algorithms and particle swarm optimization algorithm. International Journal of Systems Assurance Engineering and Management, 6(3), 248–258.CrossRef

Samanta, B., & Nataraj, C. (2009). Use of particle swarm optimization for machinery fault detection. Engineering Applications of Artificial Intelligence, 22(2), 308–316.CrossRef

Scott-Hayward, S., & Garcia-Palacios, E. (2014). Channel time allocation PSO for gigabit multimedia wireless networks. IEEE Transactions on Multimedia, 16(3), 828–836.CrossRef

Shi, Y., & Eberhart, R. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation (IEEE cat. no. 01th8546) (Vol. 1, pp. 81–86). New York: IEEE.CrossRef

Solihin, M. I., Tack, L. F., & Kean, M. L. (2011). Tuning of PID controller using particle swarm optimization (PSO). In Proceeding of the international conference on advanced science, engineering and information technology (Vol. 1, pp. 458–461). New York: IEEE.

Srisukkham, W., Zhang, L., Neoh, S. C., Todryk, S., & Lim, C. P. (2017). Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization. Applied Soft Computing, 56, 405–419.CrossRef

Tungadio, D., Numbi, B., Siti, M., & Jimoh, A. A. (2015). Particle swarm optimization for power system state estimation. Neurocomputing, 148, 175–180.CrossRef

Wang, L., & Yu, J. (2005). Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis. In International conference on natural computation (pp. 832–840). Berlin: Springer.CrossRef

Wang, X., & Qiu, X. (2013). Application of particle swarm optimization for enhanced cyclic steam stimulation in a offshore heavy oil reservoir. Computational Engineering, Finance, and Science. https://arxiv.org/abs/1306.4092.

Yang, J., Zhang, H., Ling, Y., Pan, C., & Sun, W. (2013). Task allocation for wireless sensor network using modified binary particle swarm optimization. IEEE Sensors Journal, 14(3), 882–892.CrossRef

Yao, J., & Han, D. (2013). Improved barebones particle swarm optimization with neighborhood search and its application on ship design. Mathematical Problems in Engineering, 2013, 175848.CrossRef

Zhang, H., Kennedy, D. D., Rangaiah, G. P., & Bonilla-Petriciolet, A. (2011). Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data. Fluid Phase Equilibria, 301(1), 33–45.CrossRef

Zhang, Y., Wang, S., & Ji, G. (2015). A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering, 2015, 931256.

Titel: Different Applications of PSO
verfasst von: Altaf Q. H. Badar
Verlag: Springer International Publishing
Buch: Applying Particle Swarm Optimization
Print ISBN: 978-3-030-70280-9

Electronic ISBN: 978-3-030-70281-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-70281-6_11