A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization

WANG Panpan; SHI Liping; ZHANG Yong; HAN Li

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 104-108

WANG Panpan, SHI Liping, ZHANG Yong, et al., “A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 104-108, 2013,

Citation:

WANG Panpan, SHI Liping, ZHANG Yong, et al., “A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 104-108, 2013,

WANG Panpan, SHI Liping, ZHANG Yong, et al., “A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 104-108, 2013,

Citation:

WANG Panpan, SHI Liping, ZHANG Yong, et al., “A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 104-108, 2013,

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A Hybrid Simplex Search and Modified Bare-bones Particle Swarm Optimization

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61005089), and the Scientific and Technological Research Foundation of China University of Mining and Technology (No.2008A020).

Received Date: 2011-12-01
Rev Recd Date: 2012-04-01
Publish Date: 2013-01-05

Abstract

Abstract

In order to enhance global convergence capability of particle swarm optimization, this paper proposes a novel hybrid algorithm, called SMMBBPSO, based on the Nelder-Mead Simplex method (SM) and a Modified bare-bones particle swarm optimization (MBBPSO). In this algorithm, a new strategy based on K-means clustering is proposed to combine the powerful global search capability of MBBPSO and the high accurate local search capability of SM. This makes the proposed algorithm achieve a nice balance between exploitation and exploration capability. Meanwhile, an adaptive reinitialization strategy on inactive particles is proposed to help the swarm get away from local optimal positions. Finally, simulation results on benchmark functions demonstrate the effectiveness of the proposed algorithm.
- Bare-bones particle swarm optimization,
- Nelder-Mead simplex method,
- Inactive particle,
- K-means clustering

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References(15)

References

J. Kennedy, R.C. Eberhart, “Particle swarm optimization”,Proc. of IEEE International Conference on Neural Networks,Piscataway, New Jersey, pp.1942-1948, 1995.

J. van Ast, R. Babuška, B. De Schutter, “Particle swarms in optimizationand control”, Proc. of 17th World Congress InternationalFederation of Automatic Control, Seoul, Korea, pp.5131-5136, 2008.

Q. Lü, S.R. Liu, “A particle swarm optimization algorithmwith fully communicated information”, Acta Electronica Sinica,Vol.38, No.3, pp.664-667, 2010. (in Chinese)

X.J. Wu, Z.Z. Yang, M. Zhao, “A uniform searching particleswarm optimization algorithm”, Acta Electronica Sinica,Vol.39, No.6, pp.1261-1266, 2011. (in Chinese)

Y. Zhang, D.W. Gong, Y.Q. Ren, J.H. Zhang, “Barebonesmulti-objective particle swarm optimizer for constrained optimizationproblems”, Acta Electronica Sinica, Vol.39, No.6,pp.1436-1440, 2011. (in Chinese)

Y. Tian, D.Y. Liu, “A hybrid particle swarm optimizationmethod for flow shop scheduling problem”, Acta ElectronicaSinica, Vol.39, No.5, pp.1087-1093, 2011. (in Chinese)

J. Kennedy, “Bare bones particle swarms”, Proc. of IEEESwarm Intelligence Symposium, Indianapolis, Indiana, USA,pp.80-87, 2003.

M.G.H. Omran, A.P. Engelbrecht, A. Salman, “Bare bones differentialevolution”, European Journal of Operational Research,Vol.196, No.1, pp.128-139, 2009.

R.A. Krohling, E. Mendel, “are bones particle swarm optimizationwith Gaussian or Cauchy jumps”, Proc. of IEEE Congresson Evolutionary Computation, Trondheim, Norway, pp.3285-3291, 2009.

H. Zhang, D.D. Kennedy, G.P. Rangaiah, A. Bonilla-Petriciolet,“Novel bare-bones particle swarm optimization and its performancefor modeling vapor-liquid equilibrium data”, Fluid PhaseEquilibria, Vol.301, No.1, pp.33-45, 2011.

M. Clerc, J. Kennedy, “The particle swarm-explosion, stability,and convergence in a multidimensional complex space”,IEEE Transactions on Evolutionary Computation, Vol.6, No.1,pp.58-73, 2002.

J.A. Nelder, R. Mead, “A simplex method for function minimization”,Computer Journal, Vol.7, No.4, pp.308-313, 1965.

C.C. Hsu, C.H. Gao, “Particle swarm optimization incorporatingsimplex search and center particle for global optimization”,Proc. of IEEE Conference on Soft Computing in IndustrialApplications, Muroran, JAPAN, pp.26-31, 2008.

F.Wang, Y.H. Qiu, “A novel particle swarm algorithm using thesimplex method operator”, Journal of Information and Control,Vol.34, No.5, pp.517-522, 2005. (in Chinese)

J.E. Dennis Jr, D.J. Woods, “Optimization on microcomputers:The Nelder-Meade simplex algorithm”, in: A.Wouk, New ComputingEnvironments: Microcomputers in Large Scale Computing,SIAM, Philadelphia, USA, pp.116-122, 1987.

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