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A distributed parallel firefly algorithm with communication strategies and its application for the control of
Jie Shan1, Jeng-Shyang Pan2, Cheng-Kuo Chang1
1School of Electronic Engineering and Physics, Fujian University of Technology, Fuchou, Fujian, China.
ISA Transactions
|January 24, 2021
Summary
A new distributed parallel firefly algorithm (DPFA) enhances optimization speed and accuracy. DPFA improves performance in complex problems and wind turbine control, outperforming existing methods.
Area of Science:
- Computational Intelligence
- Nature-Inspired Optimization Algorithms
Background:
- The firefly algorithm (FA) is a popular meta-heuristic optimization technique.
- FA faces limitations in solution accuracy and speed for certain optimization tasks.
Purpose of the Study:
- To introduce a distributed parallel firefly algorithm (DPFA) to overcome FA's shortcomings.
- To enhance optimization accuracy and solution speed through parallel processing and novel communication strategies.
Main Methods:
- Implemented a distributed parallel technique dividing fireflies into subgroups.
- Introduced four communication strategies for inter-subgroup information exchange: max/avg within and between groups.
- Evaluated DPFA performance against established optimization algorithms using the CEC2013 test suite.
Main Results:
- DPFA demonstrated superior competitiveness on the CEC2013 benchmark test suite.
- Applied DPFA to PID parameter tuning for variable pitch wind turbines.
- Experimental results confirmed DPFA's superior performance compared to other algorithms in wind turbine control.
Conclusions:
- DPFA effectively addresses the limitations of the standard FA, offering improved accuracy and speed.
- DPFA successfully optimizes PID parameters for wind turbines, leading to smoother power output and reduced grid impact during wind fluctuations.
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