期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 70, 期 -, 页码 159-169出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2018.01.009
关键词
Swarm intelligence; Particle swarm optimization; Crossover operation; Global optimization
类别
资金
- National Key Research and Development Program of China [2016YFB0800602, 2016YFB0800604]
- Beijing City Board of Education Science and Technology Key Project [KZ201510015015]
- Technological Innovation Team of Henan University of Science and Technology [2015XTD011]
- Key Technologies Research and Development Program of Henan Province [162102210047]
- Cooperative Engagement Fund of Henan University of Science and Technology [2015ZDCXY03]
A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed in order to breed promising exemplars. By performing crossover on the personal historical best position of each particle, the effective guiding exemplars are constructed and they maintain a good diversity. In turn, these high quality exemplars are used to guide the evolution of particles. PSOCO is two-layer particle swarm optimization with positive feedback mechanism. In order to test the performance of PSOCO, we use a set of widely used benchmark functions. The experimental results demonstrate that the proposed PSOCO is a competitive optimizer in terms of both solution quality and efficiency.
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