TY - JOUR
T1 - A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine
AU - Shan, Jie
AU - Pan, Jeng Shyang
AU - Chang, Cheng Kuo
AU - Chu, Shu Chuan
AU - Zheng, Shi Guang
PY - 2021/1/14
Y1 - 2021/1/14
N2 - Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.
AB - Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.
KW - Communication strategies
KW - Distributed parallel firefly algorithm
KW - Firefly algorithm
KW - Variable pitch control
KW - Wind turbine
UR - http://www.scopus.com/inward/record.url?scp=85099630886&partnerID=8YFLogxK
U2 - 10.1016/j.isatra.2021.01.026
DO - 10.1016/j.isatra.2021.01.026
M3 - Article
AN - SCOPUS:85099630886
JO - ISA Transactions
JF - ISA Transactions
SN - 0019-0578
ER -