101. 基于黄金正弦与自适应融合的蜉蝣优化算法.
- Author
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王 义, 张达敏, 张琳娜, 黎道花, and 邹诚诚
- Subjects
- *
ALGORITHMS , *MATHEMATICAL optimization , *VELOCITY , *ENGINEERS - Abstract
In order to solve the shortcoming of meta-heuristic mayfly optimization algorithm ( MOA ), such as low precision, slow convergence velocity and low stability, this paper proposed a new mayfly optimization algorithm, which combined the gold sine and adaptive merge. Firstly, it introduced the adaptive inertia weight factor to enhance the search and development ability of the algorithm to achieve a better balance. Then it introduced the fusion Levy flight strategy and the gold sine factor to further improve the shortcoming of fall into the local optimum, enhanced the population diversity and jumped out of the local optimum. Simulation results show that the solution precision, convergence velocity and optimization ability of the improved algorithms are significantly improved on the test functions. At the same time, in order to verify the reliability and validity of the results, this paper analyzed the data obtained by the improved algorithm on statistical test, mean absolute error and solution success rate. The algorithm has obvious improvement in the stability, reliability and robustness compared with the MOA. Besides, this paper introduced a specific engineer case to verify the applicability of the algorithm in engineering. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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