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Articles

A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

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Pages 2308-2329 | Received 18 May 2015, Accepted 30 May 2017, Published online: 27 Oct 2017

References

  • A. Alfi, PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems, Acta Automat. Sinica 37(5) (2011), pp. 541–549. doi: 10.1016/S1874-1029(11)60205-X
  • A. Alfi, Chaos suppression on a class of uncertain nonlinear chaotic systems using an optimal H adaptive PID controller, Chaos Solitons Fractals 42(3) (2012), pp. 351–357. doi: 10.1016/j.chaos.2012.01.001
  • A. Alfi and M.M. Fateh, Identification of nonlinear systems using modified particle swarm optimization: A hydraulic suspension system, Veh. Syst. Dyn. 49(6) (2011), pp. 871–887. doi: 10.1080/00423114.2010.497842
  • A. Alfi and M.M. Fateh, Intelligent identification and control using improved fuzzy particle swarm optimization, Expert Syst. Appl. 38 (2011), pp. 12312–12317. doi: 10.1016/j.eswa.2011.04.009
  • A. Alfi, A.A. Kalat, and M.H. Khooban, Adaptive fuzzy sliding mode control for synchronization of uncertain non-identical chaotic systems using bacterial foraging optimization, J. Intell. Fuzzy Syst. 26 (2014), pp. 2567–2576.
  • A. Alfi and A. Khosravi, Constrained nonlinear optimal control via a hybrid BA-SD, Int. J. Eng. 25(3) (2012), pp. 197–204.
  • A. Alfi, A. Khosravi, and A. Lari, Swarm-based structure-specified controller design for bilateral transparent teleoperation systems via µ synthesis, IMA J. Math. Control Inf. 31 (2014), pp. 111–136. doi: 10.1093/imamci/dnt005
  • A. Alfi and H. Modares, System identification and control using adaptive particle swarm optimization, Appl. Math. Model. 35 (2011), pp. 1210–1221. doi: 10.1016/j.apm.2010.08.008
  • P. Alotto, A.V. Kuntesvich, C. Magele, G. Molinari, C. Paul, M. Repetto, and K. Richter, Multiobjective optimization in magnetostics a proposal for a bench mark problem, IEEE Trans. Magn. 32 (1996), pp. 1238–1241. doi: 10.1109/20.497468
  • A. Arab and A. Alfi, An adaptive gradient descent-based local search in memetic algorithm for solving engineering optimization problems, Inf. Sci. 299 (2015), pp. 117–142. doi: 10.1016/j.ins.2014.11.051
  • M.S. Arumugam and M.V.C. Rao, On the improved performance of the particle swarm optimization algorithm with adaptive parameters cross over operators and root mean square variants for computing optimal control of class of hybrid system, Appl. Soft. Comput. 8 (2008), pp. 324–336. doi: 10.1016/j.asoc.2007.01.010
  • F.V.D. Bergh, An analysis of particle swarm optimizers, PhD, University of Pretoria, 2001.
  • A. Chatterjee and P. Siarry, A nonlinear inertia weight variation for dynamic adaption in particle swarm optimization, Comput. Oper. Res. 33 (2006), pp. 859–871. doi: 10.1016/j.cor.2004.08.012
  • J. Chuanwen and E. Bompard, A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization, Math. Comput. Simul. 68(1) (2005), pp. 57–65. doi: 10.1016/j.matcom.2004.10.003
  • M. Clerc and M. Kennedy, The particle swarm explosion, stability, and convergence in a multi dimensional complex space, IEEE Trans. Evol. Comput. 6(1) (2002), pp. 58–73. doi: 10.1109/4235.985692
  • R.C. Eberhart and Y.H. Shi, Comparing inertia weight and constriction factors in particle swarm optimization, IEEE Congress on Evolutionary Computation Vol. 1, 2000, pp. 84–88.
  • R.C. Eberhart and Y.H. Shi, Particle swarm optimization developments application and resources, Proc. IEEE Congr. Evol. Comput, Vol. 1, Seoul, Korea, 2001, pp. 81–86.
  • R.C. Eberhart and Y.H. Shi, Tracking and optimizing dynamic system with particle swarms, Congress on Evolutionary Computational, Vol. 1, Korea, 2001, pp. 94–100.
  • S.K.S. Fan and Y.Y. Chiu, A decreasing inertia weight particle swarm optimizer, Eng. Optim. 39(2) (2007), pp. 203–228. doi: 10.1080/03052150601047362
  • Y. Feng, G.F. Teng, A.X. Wang, and Y.M. Yao, Chaotic inertia weight in particle swarm optimization, Second Int. Conf on Innovative Computing, Information and Control, ICICIC’07, 2007, September, IEEE, pp. 475–478.
  • Y. Feng, Y.M. Yao, and A.X. Wang, Comparing with chaotic inertia weights in particle swarm optimization, International Conference on Machine Learning and Cybernetics, Vol. 1, IEEE, 2007, pp. 329–333.
  • J.J. Jamian, M.N. Abdullah, H. Mokhlis, M.W. Mustafa, and A.H.A. Bakar, Global particle swarm optimization for high dimension numerical functions analysis, J. Appl. Math. 2014 (2014), pp. 1–14. doi: 10.1155/2014/329193
  • B. Jiao, Z. Lian, and X. Gu, A dynamic inertia weight particle swarm optimization algorithm, Chaos Solitons Fractals 37(3) (2008), pp. 698–705. doi: 10.1016/j.chaos.2006.09.063
  • J. Kennedy and R.C. Eberhart, Particle swarm optimization, Proc. IEEE Int. Conf. on Neural Network, 1995, pp. 1942–1948.
  • M.H. Khooban, A. Alfi, and D.N.M. Abadi, Teaching-learning-based optimal interval type-2 fuzzy PID controller design: A nonholonomic wheeled mobile robots, Robotica 31(7) (2013), pp. 1059–1071. doi: 10.1017/S0263574713000283
  • T.Y. Lee and C.L. Chen, Unit commitment with probabilistic reserve: An IPSO approach, Energy Convers. Manage. 48 (2007), pp. 486–493. doi: 10.1016/j.enconman.2006.06.015
  • X. Li, Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization, Genetic and Evolutionary Computation–GECCO, Springer, Berlin Heidelberg, 2004, pp. 105–116.
  • Y. Li and X. Chen, Mobile robot navigation using particle swarm optimization and adaptive NN, Advances in natural Computation, Springer, Berlin Heidelberg, 2005, pp. 628–631.
  • J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput. 10(3) (2006), pp. 281–295. doi: 10.1109/TEVC.2005.857610
  • H. Lu and X. Chen, A new particle swarm optimization with dynamic inertia weight for solving constrained optimization problems, Inf. Technol. J. 10 (2011), pp. 1536–1544. doi: 10.3923/itj.2011.1536.1544
  • V. Mukherjee and S.P. Ghoshal, Intelligence particle swarm optimized fuzzy PID controller for AVR system, Electric Power Syst. Res. 77 (2007), pp. 1689–1698.
  • A. Nickabadi, M. Ebadzadeh, and R. Safabakhsh, DNPSO, A dynamic inching particle swarm optimizer for multi-modal optimization, IEEE World Congr. Comput. Intell. (WCCI), 2008, pp. 26–32.
  • A. Nickabadi, M.M. Ebadzadeh, and R. Safabakhsh, Evaluating the performance of DNPSO in dynamic environments, IEEE Int. Conf. on Systems, Man, and Cybernetics, Singapore, 2008, pp. 12–15.
  • A. Nickabadi, E.M. Mohammad, and R. Safabakhsh, A novel particle swarm optimization algorithm with adaptive inertia weight, Appl. Soft. Comput. 11(4) (2011), pp. 3658–3670. doi: 10.1016/j.asoc.2011.01.037
  • Y. Niu and L. Shen, An adaptive multi-objective particle swarm optimization for color image fusion, Simulated Evolution and Learning, Springer, Berlin Heidelberg, 2006, pp. 473–480.
  • A. Ratnaweera, K. Saman, and K.S. Halgamuge, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput. 8(3) (2004), pp. 240–255. doi: 10.1109/TEVC.2004.826071
  • Y.H. Shi, and R.C. Eberhart, A modified particle swarm optimizer, Proc. IEEE Int. Conf. on Evolutionary Computation, Anchorage, AK, 1998, pp. 69–73.
  • Y.H. Shi and R.C. Eberhart, Experimental study of particle swarm optimization, The 4th World Multi Conference on Systemic, Cybernetics and Informatics, Orlando, 2000.
  • H. Shokri-Ghaleh and A. Alfi, A comparison between optimization algorithms applied to synchronization of bilateral teleoperation systems against time delay and modeling uncertainties, Appl. Soft. Comput. 24 (2014), pp. 447–456. doi: 10.1016/j.asoc.2014.07.020
  • N. Singh, S. Singh, S.B. Singh, and S. Arora, Half mean particle swarm optimization algorithm, Int. J. Sci. Eng. Res. 3(8) (2012), pp. 1–9.
  • C.T. Su and J.T. Wong, Designing MIMO controller by neuro-traveling particle swarm optimizer approach, Expert Syst. Appl. 32(3) (2007), pp. 848–855. doi: 10.1016/j.eswa.2006.01.023
  • P. Tawdross and A. Konig, Local parameters particle swarm optimization, Proc. of the sixth Int. Conf. on Hybrid Intelligent System, 2006, pp. 52–55.
  • H. Yang, Z. Yang, A. Tian, Y. Li, and L. Zhang, Particle swarm optimization based on adaptive mutation and diminishing inertia weights, ninth Int. Conf on Natural Computation (ICNC), IEEE, 2013, pp. 549–553.
  • M. Yashar and A. Alifi, A memetic algorithm applied to trajectory control by tuning of fractional order proportional-integral-derivative controllers, Appl. Soft. Comput. 36, (2015), pp. 599–617. doi: 10.1016/j.asoc.2015.08.009
  • W. Yi, M. Yao, and Z. Jiang, Fuzzy particle swarm optimization clustering and its application to image clustering, Advances in Multimedia Information Processing-PCM 2006, Springer, Berlin, Heidelberg, 2006, pp. 459–467.
  • Z. You, W. Chen, G. He, and X. Nan, Adaptive weight particle swarm optimization algorithm with constriction factor, Int. Conf of Information Science and Management Engineering (ISME), Vol. 2, IEEE, 2010, August, pp. 245–248.
  • W. Zhang and Y. Liu, Adaptive particle swarm optimization for reactive power and voltage control in power systems, Advances in Natural Computation, Springer, Berlin Heidelberg, 2005, pp. 449–452.
  • TEAM Optimization Benchmark Problem 22, [Online] Available at http://www.compumag.org/jsite/images/stories/TEAM/problem22.pdf

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