|Table of Contents|

Improved particle swarm optimization algorithm for solving complex optimization problems


Research Field:
Publishing date:


Improved particle swarm optimization algorithm for solving complex optimization problems
Tang Kezong13Li Huiying2Li Juan2Luo Limin1
1.School of Computer Science and Engineering,Southeast University,Nanjing 210018,China; 2.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 3.Loboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry Education,NUST,Nanjing 210094,China
particle swarm optimization optimization strategies optimization problems particles searching cognitive coefficients social coefficients
To improve the efficiency of the particle swarm optimization algorithm for particles searching optimal solutions,an improved particle swarm optimization(IPSO)algorithm is proposed based on the standard PSO.Each particle has a corresponding grading standard by trajectory analysis of flight path,and two dynamic models of coefficients are designed for the cognitive coefficient and social one,respectively.In addition,through the introduction of the migration strategy,the newly obtained particles are more likely closer to the global optimal solution to a certain extent,and it is easy to jump out of the local optimal solution to search for the optimal solution.Simulation results show the IPSO algorithm has powerful optimizing ability and higher search veracity.The experimental data on the test criterion verify the effectiveness and the feasibility of the improved algorithm.


[1] Eberhart R C,Kennedy J.A new optimizer using particle swarm theory[A].Proceedings of the Sixth International Symposium on Mircromachine and Human Science[C].Piscataway,NJ:IEEE Press,1995:39-43.
Zhang Li,Lu Jingui,Cao Lingyan,et al.Resolution and analysis of overlapping Raman signals based on particle swarm optimization algorithm[J].Journal of Nanjing University of Science and Technology,2014,38(4):501-505.
[3]Ramazan C.A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm[J].Nuclear Engineering and Design,2011,241(5):1899-1908.
Chen Zhimin,Bo Yuming,Wu Panlong,et al.Novel lanscape addptive particle filter algorithm based on convergent particle swarm and its application[J].Journal of Nanjing University of Science and Technology,2012,36(5):861-868.
[5]Mulpuru S K,Kollu K C.Intelligent route planning for multiple robots using particle swarm optimization[A].Proceedings of International Conference on Computer Technology and Development[C].Piscataway, NJ:IEEE Press,2009:15-18.
Ai Jieqing,Gao Ji.Particle swarm optimization based on Boltzmann learning strategy[J].Journal of Nanjing University of Science and Technology,2012,36(3):402-407.
Liu Yanmin,Zhao Qingzhen.Particle swarm optimizer based on dynamic neighborhood topology and mutation operator[J].Control and Decision,2010,25(7):354-361.
[8]Ratnaweera A,Halgamuge S K,Watson H.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[9]Riget J,Vesterstom.A diversity-guided particle swarm optimizer—the ARPSO.EVALife Technical Report[R].no.2002-02.
[10]Cai X J,Cui Z H,Zeng J C,et al.Dispersed particle swarm optimization[J].Information Processing Letters,2008,105(6):231-235.
[11]Zhan Z H,Zhang J,Li Y,et al.Adaptive particle swarm optimization[J].IEEE Transactions on Systems,Man,and Cybernetics—Part B:Cybernetics,2009,39(6):1362-1381.
Tang Kezong,Xiao Xuan,Jia Jianhua,et al.Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity[J].Journal of Nanjing University of Science and Technology,2013,37(3):344-349.
[14]Liu Y,Qin Z,Shi Z W,et al.Center particle swarm optimization[J].Neurocomputing,2007,70(4-6):672-679.


Last Update: 2015-08-31