CCESC:用于全球和水库生产优化的交叉增强逃生算法
1Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
新的Crisscross逃生算法 (CCESC) 通过改善通信和搜索多样性来提高全球优化. 它在复杂的工程和水库生产优化问题上取得了卓越的结果.
科学领域:
- 计算智能
- 优化算法
- 在大自然中获得灵感的计算
背景情况:
- 全球优化在科学和工程方面至关重要.
- 像ESC这样的现有算法在通信和搜索多样化方面存在局限性.
- 在复杂的搜索场景中,
研究的目的:
- 引入Crisscross逃逸算法 (CCESC) 以提高全球优化.
- 在优化中改善代理人间的沟通和搜索轨迹多样化.
- 验证CCESC在基准和现实工程问题上的表现.
主要方法:
- 开发了Crisscross逃生算法 (CCESC),其中包含了Crisscross (CC) 信息交换机制.
- 在CEC2017基准套件和标准工程设计问题上验证了CCESC.
- 将CCESC与知名的元启发算法进行比较,并将其应用于水库生产优化.
主要成果:
- 在各种基准功能中,CCESC表现优异或具有高度竞争力.
- 克里斯克罗斯 (CC) 机制促进了更丰富的解决方案空间探索和更快的融合.
- 在储水池生产优化方面,CCESC显著提高了净现值.
结论:
- CCESC是一个强大而有效的生物灵感算法,用于全球优化.
- 通过CC信息交换机制,可以加强勘探并规避局部最佳情况.
- 对于复杂的现实世界优化挑战,CCESC显得非常有前途,尤其是在水库工程领域.
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