A Review of Particle Swarm Optimization
Review Paper
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Abstract
This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995–2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.
Keywords
Iteration Optimization Particles PSOReferences
- 1.J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks (Perth, Australia) (IEEE Service Center, Piscataway, NJ, 1995), pp. 1942–1948Google Scholar
- 2.K.E. Parsopoulos, M.N. Vrahatis, Recent approaches to global optimization problems through particle swarm optimization. J. Nat. Comput. 1, 235–306 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
- 3.K. Kameyama, Particle swarm optimization—a survey. Inst. Electron. Inf. Commun. Eng. E92-D, 1354–1361 (2009)Google Scholar
- 4.C.A. Floudas, C.E. Gounaris, A review of recent advances in global optimization. J. Global Optim. 45, 3–38 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
- 5.Y. Zhang, S. Wang, G. Ji, A comprehensive survey on particle swarm optimization algorithm and its applications, published in Hindawi, Math. Probl. Eng. 2015, 1–38 (2015)Google Scholar
- 6.Z. You, W. Chen, X. Nan, Adaptive weight Particle Swarm Optimization Algorithm with Constriction factor, in Proceedings of International Conference of Information Science and Management Engineering (2010), pp. 245–248. https://doi.org/10.1109/isme.2010.234
- 7.J.C. Bansal, P.K. Singh, M. Saraswat, A. Verma, S.S. Jadon, A. Abraham, Inertia weight strategies in particle swarm optimization, in 2011 Third World Congress on Nature and Biologically inspired Computing (IEEE, 2011), pp. 633–640, 978-1-4577-1124-4/11/©Google Scholar
- 8.M.R. Bonyadi, Z. Michalewicz, Impacts of coefficients on movement patterns in the particle swarm optimization algorithm. IEEE Trans. Evolut. Comput. 21(3), 378–390 (2017)Google Scholar
- 9.K. Zielinski, R. Laum, Stopping criteria for a constrained single-objective particle swarm optimization algorithm. Informatica 31, 51–54 (2007)zbMATHGoogle Scholar
- 10.Q. Wu, C. Cole, T. McSweeng, Applications of particle swarm optimization in the railway domain. Int. J. Rail Transp. 4(3), 167–190 (2016)CrossRefGoogle Scholar
- 11.M.R. Al Rashidi, M.E. El-Hawary, A survey of particle swarm optimization applications in electric power systems. IEEE Trans. Evolut. Comput. 13(4), 913–918 (2016)CrossRefGoogle Scholar
- 12.N.K. Jain, U. Nangia, A. Jain, PSO for multiobjective economic load dispatch (MELD) for minimizing generation cost and transmission losses. J. Inst. Eng. (India) Ser. B 97(2), 185–191 (2016)CrossRefGoogle Scholar
- 13.M.A. Abido, Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)CrossRefGoogle Scholar
- 14.R.-H. Liang, R.-H. Liang, Y.-T. Chen, W.-T. Tseng, Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr. Power Syst. Res. 81(7), 1466–1474 (2011)CrossRefGoogle Scholar
- 15.C.P. Salomon, G. Lambert-Torres, H.G. Martins, C. Ferreira, C.I.A., Costa Load flow computation via particle swarm optimization, in 9th IEEE/IAS International Conference on Industry Applications (INDUSCON) (2010), 8–10 Nov 2010, pp. 1–6Google Scholar
- 16.P. Acharjee, S.K. Goswami, Chaotic particle swarm optimization based reliable algorithm to overcome the limitations of conventional power flow methods, in Power Systems Conference and Exposition, 2009. PSCE ‘09. IEEE/PES, 15–18 March 2009, pp. 1–7Google Scholar
- 17.Z.L. Gaing, A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)CrossRefGoogle Scholar
- 18.H. YapJcJ, N. Çetinkaya, An improved particle swarm optimization algorithm using eagle strategy for power loss minimization. Math. Probl. Eng. (2017). https://doi.org/10.1155/2017/1063045. (Article ID 1063045) MathSciNetGoogle Scholar
- 19.A. Nimtawat, P. Nanakom, Simple particle swarm optimization for solving beam-slab layout design problems. Elsevier 14, 1392–1398 (2011)Google Scholar
- 20.T.T. Mac, C. Copot, D.T. Tran, R. De Keyser, A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl. Soft Comput. 59, 68–76 (2017)CrossRefGoogle Scholar
- 21.M.J. Islam, X. Li, Y. Mei, A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl. Soft Comput. 59, 182–196 (2017)CrossRefGoogle Scholar
- 22.A. Suresha, K.V. Harisha, N. Radhika, Particle swarm optimization over back propagation neural network for length of stay prediction. Procedia Comput. Sci. 46, 268–275 (2015)CrossRefGoogle Scholar
- 23.R. Zoi, V. Kalivarapu, E. Winer, J. Oliver, S. Bhattacharya, Particle swarm optimization based source seeking. IEEE Trans. Autom. Sci. Eng. 12(3), 865–875 (2015)CrossRefGoogle Scholar
- 24.P. Wen, M. Zhi, G. Zhang, S. Li, Fault prediction of elevator door system based on PSO-BP neural network, Scientific Research Publishing, Engineering 8, 761–766 (2016). ISSN Online: 1947-394X, ISSN Print: 1947-3931Google Scholar
- 25.T. Gong, A.L. Tuson, Particle swarm optimization for quadratic assignment problems—a forma analysis approach. Int. J. Comput. Intell. Res. 2, 1–9 (2007)Google Scholar
- 26.Z. Liu, R. Zhao, Equipment possession quantity modelling and particle swarm optimization, in Proceedings of Third IEEE International Conference on Genetic Evolutionary Computing (2009), pp. 628–632. https://doi.org/10.1109/wgec
- 27.J.-Q. Li, Q.-K. Pank, B.-X. Jia, Y.-T. Wang, A hybrid particle swarm optimization and tabu search algorithm for flexible job-shop scheduling problem. Int. J. Comput. Theory Eng. 2(2), 1793–8201 (2010)Google Scholar
- 28.B. Bhushan, S.S. Pillai, Particle swarm optimization and firefly algorithm: performance analysis, in 2013 3rd IEEE International Advance Computing Conference (IACC) (IEEE, 2013), pp. 746–751, 978-1-4673-4529-3/12Google Scholar
- 29.P.J. Angeline, Using Selection to Improve Particle Swarm Optimization (Natural Selection Inc, Vestal) (1998), pp. 84–89Google Scholar
- 30.Y.-P. Chen, W.-C. Peng, Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(6), 1460–1470 (2007)CrossRefGoogle Scholar
- 31.W. Jaio, G. Liu, D. Liu, Elite particle swarm optimization with mutation, in 2008 Asia simulation Conference—Proceedings of IEEE 7th International Conference on Systems Simulation and Scientific Computing (2008), pp. 800–803Google Scholar
- 32.S. Song, Shujun et al., Improved particle swarm cooperative optimization algorithm based on chaos & simplex method, in Proceedings o f Second IEEE International Workshop on Education Technology and Computer Science (2010). https://doi.org/10.1109/etcs.2010.235.10
- 33.M. Chen, T. Wang, J. Feng, Y.-Y. Tang, L.-X. Zhao, A hybrid particle swarm optimization improved by mutative scale chaos algorithm, in Fourth International Conference on Computational and information Sciences (IEEE, 2012), pp. 321–324, 978-0-7695-4789-3/12 ©. https://doi.org/10.1109/iccis.2012.19
- 34.J. Liu, B. Zhu, The application of particle swarm optimization algorithm in the extremum optimization of nonlinear function, in 12th IEEE International Conference on Computer and Information Technology (IEEE, 2012), pp. 286–289,978-0-7695-4858-6/12 ©. https://doi.org/10.1109/cit.2012.74
- 35.A.M. Sharaf, A.A.A. Ei-Gammal, A Novel Discrete Multi-objective Particle Swarm Optimization (MOPSO) of Optimal Shunt Power Filter (IEEE, 2009), 978-1-4244-3811-2/09Google Scholar
- 36.C.K. Goh, K.C. Tan, D.S. Liu, S.C. Chaim, A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Eur. J. Oper. Res. 202, 42–54 (2010)CrossRefzbMATHGoogle Scholar
- 37.K.R. Harrison, B. Ombuki-Berman, A.P. Engelbrecht, Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization. Technical Report (Brock University, 2012)Google Scholar
- 38.M. Benedetti, A. Massa, Memory enhanced PSO-based optimization approach for smart antennas control in complex interference scenarios. IEEE Trans. Antennas Prop. Mag. 56(7), 1939–1947 (2008)CrossRefGoogle Scholar
- 39.H. Duan, P. Li, Y. Yu, A predator-prey Particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory. IEEE/CAA J. Autom. Sin. 2(1), 11–18 (2015)MathSciNetCrossRefGoogle Scholar
- 40.J.J. Liang, A.K. Qin, P.N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evolut. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
- 41.C. Li, S. Yang, T.T. Nguyen, A self-learning particle swarm optimizer for global optimization problems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(3), 627–646 (2012)CrossRefGoogle Scholar
- 42.Z.-H. Zhan, J. Zhang, Y. Li, Y.-H. Shi, Orthogonal learning particle swarm optimization. IEEE Trans. Evolut. Comput. 15(6), 832–847 (2011)CrossRefGoogle Scholar
- 43.J.F. Schutteand, A.A. Groenwold, A study of global optimization using particle swarms. J. Glob. Optim. 31, 93–108 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
- 44.W.-B. Liu, X.-J. Wang, An evolutionary game based particle swarm optimization algorithm. J. Comput. Appl. Math. 214, 30–35 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
- 45.S. Hossen, F. Rabbi, M. Rahman, Adaptive particle swarm optimization (APSO) for multimodal function optimization. Int. J. Eng. Technol. 1(3), 98–103 (2009)Google Scholar
- 46.B. Benmessahel, M. Touahria, An improved combinatorial particle swarm optimization algorithm to database verticle partition. J. Emerg. Trends Comput. Inf. Sci. 2(3), 130–135 (2010), ISSN 2079-8407Google Scholar
- 47.W. Jii, K. Wangi, An improved particle swarm optimization algorithm, in 2011 International Conference on Computer Science and Network Technology (IEEE, 2011), pp. 585–589, 978-1-4577-1587-7111/$26.00 ©Google Scholar
- 48.Z. Beheshti, S.M. Shamsuddin, S.S. Yuhaniz, Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems. J. Glob. Optim. 57, 549–573 (2013). https://doi.org/10.1007/s10898-012-0006-1 MathSciNetCrossRefzbMATHGoogle Scholar
- 49.L.M. Rios, N.V. Sahinidis, Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Glob. Optim. 56, 1247–1293 (2013). https://doi.org/10.1007/s10898-012-9951-y MathSciNetCrossRefzbMATHGoogle Scholar
- 50.Z. Chen, Y. Bo, P. Wu, W. Zhou, A new particle filter based on organizational adjustment particle swarm optimization. Appl. Math. Inf. Sci. 7(1), 179–186 (2013)MathSciNetCrossRefGoogle Scholar
- 51.M.A. Arasomwan, A.O. Adewumi, An Adaptive Velocity Particle Swarm Optimization for High-Dimensional Function Optimization Congress on Evolutionary Computation, June 20–23, Cancún, México (IEEE, 2013)Google Scholar
- 52.L. Baiqum, G. Gaiquin, L. Zeyu, The block diagram method for designing the particle swarm optimization. J. Glob. Optim. 52(689), 710 (2012)MathSciNetGoogle Scholar
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