A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine. (September 2021) - Shan, Jie; Pan, Jeng-Shyang; Chang, Cheng-Kuo; Chu, Shu-Chuan; Zheng, Shi-Guang
Abstract: 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. Highlights: Developed distributed parallel mechanism as a method to balances the exploration and exploitation of algorithms. A proposed distributed parallel firefly algorithm (DPFA) improves the performance of the original FA. Compared DPFA with other well-known optimization algorithms under the test suite of CEC2013. DPFA is applied in the control of variable pitch wind turbine to tune PID parameters.