- Title
- An improved particle swarm optimization algorithm combined with invasive weed optimization
- Creator
- Zhao, Huan; Wang, Xin; Jiao, Zhongze; Zeng, Wen; Dou, Jinxiao; Yu, Jianglong
- Relation
- 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). Proceedings of 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) (Shenyang, China 21-23 October, 2019) p. 251-257
- Publisher Link
- http://dx.doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00070
- Publisher
- Institue of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2019
- Description
- This paper presents a hybrid algorithm based on the invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, computational results, preceded by analysis and selection of IW-PSO parameters, show that IW-PSO can improve the search performance. In the other comparative experiment with fixed iteration, the IW-PSO algorithm is compared with various more up-to-date improved PSO procedures appearing in the literature. Comparative results demonstrate that IW-PSO can generate quite competitive quality solution in stability, accuracy and efficiency. As evidenced by the overall assessment based on two kinds of computational experience, IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum.
- Subject
- particle swarm optimization; invasive weed optimization; hybrid algorithm; SDG 15; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1436003
- Identifier
- uon:39888
- Identifier
- ISBN:9781728152097
- Language
- eng
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