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Diversity enhanced particle swarm optimization with neighborhood search
- Source :
-
Information Sciences . Feb2013, Vol. 223, p119-135. 17p. - Publication Year :
- 2013
-
Abstract
- Abstract: Particle Swarm Optimization (PSO) has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a hybrid PSO algorithm, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions, including rotated multimodal and shifted high-dimensional problems. Comparison results show that DNSPSO obtains a promising performance on the majority of the test problems. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 223
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 83654262
- Full Text :
- https://doi.org/10.1016/j.ins.2012.10.012