Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

Computational Intelligence and Neuroscience
Xiao-peng WeiQiang Zhang

Abstract

We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.

References

May 31, 2001·Evolutionary Computation·N Hansen, A Ostermeier

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Citations

Feb 26, 2021·Computational Intelligence and Neuroscience·Xiang Yu, Yu Qiao

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Software Mentioned

MSCPSO
CPSO
PSO
MATLAB
CMAES
BMPSO
ANSYS

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