Vestnik KRAUNC. Fiz.-Mat. Nauki. 2022. vol. 39. no. 2. pp. 156–180. ISSN 2079-6641
MSC 68Т27
Review Article
A Concise Overview of Particle Swarm Optimization Methods
E. M. Kazakova
Institute of Applied Mathematics and Automation KBSC RAS, 360000, Nalchik, Shortanova st., 89a, Russia
E-mail: shogenovae@inbox.ru
Particle Swarm Optimization (PSO) is a meta-heuristic method of global, inferred, proposed by Kennedy and Eberhart in 1995. It is currently one of the most commonly used search methods. This review provides a brief overview of PSO research in recent years – swarm and rate initialization methods in PSO, modifications, neighborhood topologies, hybridization, and an overview of various PSO applications.
Key words: optimization, particle swarm optimization, meta-heuristic algorithm.
DOI: 10.26117/2079-6641-2022-39-2-156-180
Original article submitted: 07.07.2022
Revision submitted: 25.08.2022
For citation. Kazakova E. M. A Concise Overview of Particle Swarm Optimization Methods. Vestnik KRAUNC. Fiz.-mat. nauki. 2022, 39: 2, 156-180. DOI: 10.26117/2079-6641-2022-39-2-156-180
Competing interests. The authors declare that there are no conflicts of interest regarding authorship and publication.
Contribution and Responsibility. All authors contributed to this article. Authors are solely responsible for providing the final version of the article in print. The final version of the manuscript was approved by all authors.
The content is published under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/deed.ru)
© Kazakova E. M., 2022
References
- Eberhart R., Kennedy J. Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, 4, IEEE, 1995, pp. 1942–1948. DOI: 10.1109/ICNN.1995.488968.
- Eberhart R., Kennedy J.A new optimizer using particle swarm theory, MHS’95. Proceedings of the Sixth International Symp. on Micro Machine and Human Sci., Ieee, 1995, pp. 39-43. DOI:10.1109/MHS.1995.494215.
- Cleghorn C. W., Engelbrecht A.P. Particle swarm convergence: an empirical investigation, 2014 IEEE Congress on Evolut. Comput. (CEC), IEEE, 2014, pp. 2524-2530. DOI: 10.1007/978-3-319-09952-112.
- Banks A., Vincent J., Anyakoha C. A review of particle swarm optimization, Part I: background and development, Nat. Comput., 2007, vol. 4, no. 6, pp. 467-484. DOI: 10.1007/s11047-007-9049-5
- Karpenko A.P., Seliverstov E. Ju. A review of particle swarm methods for the global optimization problem, Mashinostr. i komp’juternye tehnol., 2009, no. 3, pp. 2. (In Russian).
- Houssein E. H., Saad M. R., Hashim F. A., Shaban H., Hassaballah M. Levy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems, Eng. Appl. Artif.Intell., 2020, vol. 94, pp. 103731. DOI: 10.1016/j.engappai.2020.103731.
- Cazzaniga P., Nobile M. S., Besozzi D. The impact of particles initialization in PSO: parameter estimation as a case in point, 2015, IEEE Conferen. on Comput. Intellig. in Bioinform. and Comp. Biology (CIBCB), 2015, vol. 94, pp. 1-8.
DOI:10.1109/CIBCB.2015.7300288. - Farooq M. U., Ahmad A., Hameed A. Opposition-based initialization and a modified pattern for inertia weight (IW) in PSO, 2017 IEEE International Confer. on Innovat. in Intel. Sys. and Appl. (INISTA), 2017, pp. 96-101. DOI: 10.1109/INISTA.2017.8001139.
- Djellali H., Ghoualmi N. Improved chaotic initialization of particle swarm applied to feature selection, 2019 Intern. Conf. on Network. and Advanc. Sys. (ICNAS), IEEE, 2019, pp. 1-5. DOI: 10.1109/ICNAS.2019.8807837.
- Li Q., Liu S.-Y., Yang X.-S. Influence of initialization on the performance of metaheuristic optimizers, Appl. Soft Comput., 2020, pp. 106193. DOI: 10.1016/j.asoc.2020.106193.
- Liang X., et al. Recent advances in particle swarm optimization via population structuring and individual behavior control, 2013 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), 2013, pp. 503-508. DOI: 10.1109/ICNSC.2013.6548790.
- Engelbrecht A. Particle swarm optimization: velocity initialization, 2012 IEEE Congress on Evolutionary Computation, IEEE, 2012, pp. 1-8. DOI: 10.1109/CEC.2012.6256112.
- Gunasundari S., Janakiraman S., Meenambal S. Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis, Expert Syst. Appl., 2016, vol. 56, pp. 28-47. DOI: 10.1016/j.eswa.2016.02.042Get
- Marandi A., et al. Boolean particle swarm optimization and its application to the design of a dual-band dual-polarized planar antenna, 2006 IEEE International Conference on Evolutionary Computation, IEEE, 2006, pp. 3212-3218. DOI: 10.1109/CEC.2006.1688716
- Shi Y., Eberhart R. C. Parameter selection in particle swarm optimization, in: International conference on evolutionary programming, 1998, pp. 591–600. DOI:10.1007/BFb0040810.
- Qu B. Y., Suganthan P. N., Das S. A distance-based locally informed particle swarm model for multimodal optimization, IEEE Trans. Evol. Comput., 2012, vol. 17, no. 3, pp. 387–402. DOI: 10.1109/TEVC.2012.2203138.
- Shi Y., Liu H., Gao L., Zhang G. Cellular particle swarm optimization, Inf. Sci., 2011, vol. 181, No 20, pp. 4460–4493. DOI: 10.1016/j.ins.2010.05.025.
- Alba E., et al. Meta-heuristics and parallelism, In book: Parallel Metaheuristics: A New Class of Algorithms, 2005, pp. 79–103 DOI: 10.1002/0471739383.ch4.
- Houssein E. H., et al. Major advances in particle swarm optimization: theory, analysis, and application, Swarm and Evol. Comp., 2021, vol. 63, pp. 100868. DOI:10.1016/j.swevo.2021.100868.
- Hu X., Eberhart R. C. Adaptive particle swarm optimization: detection and response to dynamic systems, Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02, 2, IEEE, 2002, pp. 1666–1670. DOI: 10.1109/CEC.2002.1004492.
- Xie X.-F., Zhang W.-J., Yang Z.-L. Adaptive particle swarm optimization on individual level, 6th International Conference on Signal Processing, 2002, 2, IEEE, 2002, pp. 1215–1218. DOI: 10.1109/ICOSP.2002.1180009.
- Zhan Z.-H., Zhang J., Li Y., Chung H.S.-H. Adaptive particle swarm optimization, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2009, vol. 39, no. 6, pp. 1362–1381. DOI: 10.1016/j.engappai.2020.103731.
- Ermakov B. S. Particle swarm method with adaptive social and cognitive components, Modelirovanie, optimizacija i informacionnye tehnologii, 2019, vol. 7, no. 3, pp. 6. DOI:10.26102/2310-6018/2019.26.3.006 (In Russian).
- Sun J., Feng B., Xu W. Particle swarm optimization with particles having quantum behavior, Proceedings of the 2004 Congress on Evolutionary Computation, 1, IEEE, 2004, pp. 325–331. DOI: 10.1109/CEC.2004.1330875.
- Qian Q., Wu J., Wang Z. Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm, Pers. Ubiquitous Comput, 2019, vol. 23, no. 3-4, pp. 393–403. DOI:10.1007/s00779-019-01216-1.
- Lalwani S., Sharma H., Satapathy S. C., Deep K., Bansal J. C. A survey on parallel particle swarm optimization algorithms, Arab. J. Sci. Eng., 2019, vol. 44, no. 4, pp. 2899–2923. DOI:10.1007/s13369-018-03713-6.
- Gies D., Rahmat-Samii Y. Reconfigurable array design using parallel particle swarm optimization, IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting, 2003, vol. 1, pp. 177–180. DOI: 10.1109/APS.2003.1217429.
- Baskar S., Suganthan P. N. A novel concurrent particle swarm optimization, Proc. of the 2004 Congr. on Evol. Comp., 2004, vol. 1, pp. 792–796. DOI: 10.1109/CEC.2004.1330940.
- Angeline P. J. Using selection to improve particle swarm optimization, in: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEEWorld Congress on Computational Intelligence, 1998, pp. 84–89. DOI: 10.1109/ICEC.1998.699327
- Higashi N., Iba H. Particle swarm optimization with gaussian mutation, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03, 2003 10.1109/SIS.2003.1202250, pp. 72–79. DOI: 10.1109/SIS.2003.1202250.
- Lovbjerg M., Rasmussen T.K., Krink T. Hybrid particle swarm optimiser with breeding and subpopulations, Proc. of the 3rd Ann. Conf. on Gen. and Evolut. Comput., 2001, pp. 469–476.
- Miranda V., Fonseca N. EPSO-best-of-two-worlds meta-heuristic applied to power system problems, Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02, 2002, vol. 2, pp. 1080–1085. DOI: 10.1109/CEC.2002.1004393
- Yang B., Chen Y., Zhao Z. A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems, International Conference on Control and Automation, IEEE, 2007, pp. 166–170. DOI: 10.1109/ICCA.2007.4376340.
- Robinson J., Sinton S., Rahmat-Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna, IEEE Anten. and Propagat. Society Internat. Symp., 2002, vol. 1, pp. 314–317. DOI: 10.1109/APS.2002.1016311.
- Korolev S. A., Majkov D. V. Modification of the particle swarm algorithm based on the hierarchy analysis method, Vestnik VGU. Serija: Sistem. anal. i inform. tehnol., 2019, no. 4, pp. 36-46. DOI: 10.17308/sait.2019.4/2679 (in Russian).
- Yang G., Chen D., Zhou G. A new hybrid algorithm of particle swarm optimization, Internat. Confer. on Intelli. Comp., 2006, pp. 50-60. DOI: 10.1007/118161026.
- Javidrad F., Nazari M. A new hybrid particle swarm and simulated annealing stochastic optimization method, Appl. Soft Comput., 2017, vol. 60, pp. 634–654. DOI: 10.1016/j.asoc.2017.07.023.
- Villarrubia G., De Paz J. F., Chamoso P., De la Prieta F. Artificial neural networks used in optimization problems, Neurocomp., 2018, vol. 272, pp. 10–16. DOI: 10.1016/j.neucom.2017.04.075.
- Eberhart R. C., Hu X. Human tremor analysis using particle swarm optimization, Proceedings of the 1999 congress on evolutionary computation-CEC99, 1999, vol. 3, pp. 1927–1930. DOI:10.1109/CEC.1999.785508
- Hamada M., Hassan M. Artificial neural networks and particle swarm optimization algorithms for preference prediction in multicriteria recommender systems, Inform., 2018, vol. 5, no. 2, pp. 25. DOI: 10.3390/informatics5020025
- Zeng N., et al. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease, Neurocomp., 2018, vol. 320, pp. 195-202. DOI:10.1016/j.neucom.2018.09.001.
- Huang K.W., Chen J.L., Yang C.S., Tsai C.W. A memetic particle swarm optimization algorithm for solving the dna fragment assembly problem, Neural Comput. Appl., 2015, vol. 26, no. 3, pp. 495-506. DOI:10.1007/s00521-014-1659-0.
- Karpenko A.P., Matveeva K. O., Bulanov V. A. Solving the problem of molecular docking by a modified particle swarm method, Mashinostroenie i komp’juternye tehnologii, 2014, no. 4, pp. 339-353. DOI: 10.7463/0414.0707258 (In Russian).
- Katarya R., Verma O.P. Efficient music recommender system using context graph and particle swarm, Multimed. Tools Appl., 2018, vol. 77, no. 2, pp. 2673–2687. DOI:10.1007/s11042-017-4447-x
- Manusov V. Z., Matrenin P. V., Nasrullo H. Application of swarm intelligence algorithms in the management of a generating consumer with renewable energy sources, Sistem. anal. i obrabot. dannyh, 2019, vol. 76, no. 3, pp. 115-134. DOI: 10.17212/1814-1196-2019-3-115-134 (In Russian).
- Gadasin D. V., Smal’kov N. A., Kuzin I. A. Using the Particle Swarm Method for Load Balancing in Internet of Things Networks, Sistemy sinhronizacii, formirovanija i obrabotki signalov, 2022, vol. 13, no. 2, pp. 17-23. (In Russian).
- El-Khatib S., Skobtsov Y.A., Rodzin S.I. Hyper heuristic particle swarm optimization method for medical images segmentation, Informatizaciya i svyaz’, 2021, no. 2, pp. 22-29. DOI: 10.34219/2078-8320-2021-12-2-22-29 (In Russian).
- Chastikova V.A., Vlasov K.A., Kartamyshev D.A. Obnaruzhenie DDoS-atak na osnove nejronnyh setej s primeneniem metoda roya chastic v kachestve algoritma obucheniya, Informatizaciya i svyaz’, 2014, vol. 4, no. 8, pp. 829-832. (In Russian).
- Javan S. M., Shourian M. Comparative Application of Model Predictive Control and Particle Swarm Optimization in Optimum Operation of a Large-Scale Water Transfer System. Water Resour Manage, 2021, vol. 35, pp. 707–727 DOI: 10.1007/s11269-020-02755-6
- Liu W., et al. A novel particle swarm optimization approach for patient clustering from emergency departments, IEEE Transactions on Evolutionary Computation, 2018, vol. 23, no. 4, pp. 632-644. DOI: I10.1109/TEVC.2018.2878536
Kazakova Elena Musovna – Junior Researcher of the Department of Neural Networks and Machine Learning, Institute of Applied Mathematics and Automation, Kabardino-Balkarian Republic, Nalchik, Russia, ORCID 0000-0002-5819-9396.