Dynamic Economic Emission Dispatch Based On Multiple Learning Multi-objective Pigeon-inspired Optimization
[1]Yan Li,Li Chao,Chai Xuchao,et al.Dynamic Economic Emission Dispatch Based On Multiple Learning Multi-objective Pigeon-inspired Optimization[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):2-.[doi:10.13705/j.issn.1671-6833.2019.04.023]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40
Number of periods:
2019 04
Page number:
2-
Column:
Public date:
2019-07-10
- Title:
- Dynamic Economic Emission Dispatch Based On Multiple Learning Multi-objective Pigeon-inspired Optimization
- Keywords:
- Environmental economic dispatch; Multi-objective optimization; Pigeon flock optimization; learn more; small probability variation
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.04.023
- Abstract:
- For solving the dynamic economic emission dispatch problem (DEED), a multiple learning based multi-objective pigeon-inspired optimization (MLMPIO) algorithm is proposed in this paper. In the proposed multiple learning strategy, individuals of the population are allowed to learn from multiple global best positions of the external archive and from the personal historical best positions. This learning strategy enables the preservation of diversity and global search ability of the population to prevent premature convergence. Meanwhile, small probability mutation is introduced to MLMPIO to enhance the swarm diversity and search ability further. The external archive with adaptive changing capacity is used to store the current Pareto optimal solutions. To verify the performance of the proposed method, the DEED problem of the IEEE 10-generator power system has been solved. And the results demonstrate the feasibility and effectiveness of the proposed method
- References:
- -
- Similar References:
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Last Update:
2019-07-28