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Improved differential evolution with dynamic mutation parameters
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DOI：10.1007/s00500-023-09080-1.png)
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摘要
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Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed.
Keyword:
Differential evolution (DE) Algorithm
Global optimization
Scheme optimization
Test function
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.3W
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引用论文
MODE: multiobjective differential evolution for feature selection and classifier ensemble.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation基于改进适应度的变异差分进化算法的实参数无约束优化SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
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