opposition-based barebones particle swarm for constrained nonlinear optimization problems

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ID: 224353
2012
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Abstract
This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance.
Reference Key
wang2012mathematicalopposition-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Hui Wang
Journal journal of power sources
Year 2012
DOI
10.1155/2012/761708
URL
http://dx.doi.org/10.1155/2012/761708
Keywords
civil engineering (general)mathematics engineering (general)

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