一个新的混乱和基于邻里搜索的人工蜜蜂群算法,用于解决优化问题
Wen-Sheng Xiao1,2, Guang-Xin Li1,2, Chao Liu3,4
1National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum (East China), Qingdao, 266580, China.
Scientific reports
|November 22, 2023
概括
这项研究引入了一种基于混乱和邻里搜索的新型ABC算法 (CNSABC),以改进传统的人工蜂群 (ABC) 算法. CNSABC 增强了对优化问题的融合和搜索能力.
科学领域:
- 人工智能的人工智能
- 群集情报 群集情报 群集情报
- 优化算法 优化算法
背景情况:
- 传统的人工蜂群 (ABC) 算法遭受了利用不足和缓慢的融合.
- 开发改进的启发式算法对于推进人工智能研究至关重要.
研究的目的:
- 提出一个新的ABC算法的变体,命名为混沌和邻居搜索基于ABC算法 (CNSABC).
- 解决传统的ABC算法的缺点,特别是利用不足和缓慢的融合.
主要方法:
- 引入了带有相互排斥机制的伯努利混乱映射,以增强多样性和探索.
- 整合了邻里搜索机制,并添加了压缩因子,以改善对接和利用.
- 整合了持续的蜜蜂,以进一步提高算法的性能.
主要成果:
- 实验结果表明,与传统方法相比,拟议的CNSABC表现出更高的融合效率和搜索能力.
- 这三种改进的机制各自有助于CNSABC的整体有效性.
- CNSABC成功地应用于工程优化问题,得到了令人满意的解决方案.
结论:
- 新的CNSABC算法有效地克服了传统的ABC算法的局限性.
- 对于优化任务,CNSABC提供了增强的勘探,开发和融合效率.
- 该算法在解决复杂的工程优化问题时显示出实际应用.
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