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Optimization of complex engineering problems using modified sine cosine algorithm
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DOI：10.1038/s41598-022-24840-z.png)
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摘要
En 中文
In this article, a modified version of the Sine Cosine algorithm (MSCA) is proposed to solve the optimization problem. Based on the Sine Cosine algorithm (SCA), the position update formula of SCA is redefined to increase the convergence speed, then the Levy random walk mutation strategy is adopted to improve the population diversity. In order to verify the performance of MSCA, 24 well-known classical benchmark problems and IEEE CEC2017 test suites were introduced, and by comparing MSCA with several popular methods, it is demonstrated that MSCA has good convergence and robustness. Finally, MSCA is used to address six complex engineering design problems, demonstrating the engineering utility of the algorithm.
Keyword:
PARTICLE SWARM OPTIMIZATION
ANT COLONY OPTIMIZATION
SEARCH ALGORITHM
GLOBAL OPTIMIZATION
GENETIC ALGORITHM
DESIGN
EVOLUTIONARY
GSA
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期刊
IF:
3.9
论文数:
28.3W
被引数:
89.7W
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