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.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Wei XP,Zhang JX,Zhou DS,Zhang Qdoi
10.1155/2015/904713subject
Has Abstractpub_date
2015-01-01 00:00:00pages
904713eissn
1687-5265issn
1687-5273journal_volume
2015pub_type
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