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A smart metaheuristic algorithm for solving engineering problems
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DOI：10.1007/s00366-020-00951-x.png)
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Every day, the immature sunflowers generate heliotropic movements. Two growth mechanisms are controlled on the heliotropic movements of immature sunflowers. The first mechanism is the sun-tracking phenomenon, which is caused by a growth hormone called Auxin.While the second mechanism is the biological clock which is responsible for the nocturnal reorientation of the immature sunflowers at the night. By clarifying and idealizing the growth mechanisms of the immature sunflowers in nature, a new type of nature-inspired optimization algorithm, called Smart Flower Optimization Algorithm (SFOA), is proposed in this paper. The proposed algorithm has been presented in two modes: sunny and cloudy or rainy modes depending on the weather. The SFOA is benchmarked on three parts. First, a set of 15 benchmark functions on CEC 2015 is used to test the efficiency of the SFOA using statistical analysis and Wilcoxon's test. Second, the SFOA is utilized for designing an adaptive IIR system to appropriate the unknown system. Finally, four different engineering design problems (three-bar truss, tension/compression spring, speed reducer, and welded beam) are solved by SFOA. In the first part, the SFOA is compared with well-known algorithms. The results confirm the capabilities and efficiency of the proposed algorithm in finding the optimum. The promising solutions obtained for the IIR system identification and the engineering design problems demonstrate that the SFOA significantly outperforms other algorithms and show the applicability of the proposed algorithm in solving the real-world problems with unknown search spaces as well.
Keyword:
Smart Flower Optimization Algorithm
Metaheuristic algorithms
CEC 2015 benchmark functions
IIR system identification
Engineering design problems
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