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A novel particle swarm optimizer without velocity: Simplex-PSO

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Abstract

A simplex particle swarm optimization (simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions. In simplex-PSO, the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle. The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence. In order to reduce the probability of trapping into a local optimal value, an extremum mutation was introduced into simplex-PSO and simplex-PSO-t (simplex-PSO with turbulence) was devised. Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t, and the experimental results confirmed the conclusions: (1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality; (2) compared PSO with chaos PSO (CPSO), the best optimum index increases by a factor of 1×102–1×104.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Hong-feng Xiao  (肖宏峰) & Guan-zheng Tan  (谭冠政)

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  1. Hong-feng Xiao  (肖宏峰)
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  2. Guan-zheng Tan  (谭冠政)
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Corresponding author

Correspondence to Guan-zheng Tan  (谭冠政).

Additional information

Foundation item: Project(50275150) supported by the National Natural Science Foundation of China; Project(20070533131) supported by Research Fund for the Doctoral Program of Higher Education of China

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Xiao, Hf., Tan, Gz. A novel particle swarm optimizer without velocity: Simplex-PSO. J. Cent. South Univ. Technol. 17, 349–356 (2010). https://doi.org/10.1007/s11771-010-0052-0

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  • DOI: https://doi.org/10.1007/s11771-010-0052-0

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