基于增强突变策略的差异进化,用于解决全球优化问题的问题
Pawan Mishra1, Musrrat Ali2, Pooja1
1Department of Electronics and Communication, University of Allahabad, Prayagraj, India.
PeerJ. Computer science
|March 26, 2025
概括
这项研究为差异进化 (DE) 算法引入了一种新的突变策略. 增强的突变提高了全球优化任务的融合速度和解决方案准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 差异进化 (DE) 是全球优化的一个关键算法.
- 德的性能在很大程度上依赖于其对解决方案多样性和质量的突变操作.
研究的目的:
- 探索和引入用于差异进化 (DE) 的新型突变策略.
- 为了实现勘探和开发之间的平衡,以改善融合和解决方案质量.
主要方法:
- 为DE提出了一种新的突变策略,将系数因子"σ"与"DE/rand/1"基向量结合起来.
- 该战略旨在在开发过程中加强当地趋同.
主要成果:
- 增强的突变策略显著超过现有的最先进的算法.
- 在27个基准函数的解决方案准确性和融合速度方面取得了明显的改进.
结论:
- 突变操作对于DE在全球优化中的性能至关重要.
- 提出的策略为开发更有效的DE算法提供了有价值的见解,用于复杂的问题.
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