双巴拉库达群集优化算法:一种自然灵感的元启发方法,用于高维优化问题
Jia Guo1,2, Guoyuan Zhou3, Ke Yan4
1School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.
Scientific reports
|October 25, 2023
概括
本研究介绍了PBSO算法 (Pair Barracuda Swarm Optimization) 以克服局部最佳值和维度灾难在高维度优化中. 与基准函数的最先进方法相比,PBSO表现优越.
科学领域:
- 智能计算是一种智能计算.
- 优化算法 优化算法
- 计算智能是一种计算智能.
背景情况:
- 高维优化对传统的进化算法构成重大挑战.
- 现有的方法经常遭受维度灾难和局部最佳状态的影响,降低了准确性.
- 需要新的方法来有效地导航复杂的,高维的搜索空间.
研究的目的:
- 介绍对巴拉库达群优化 (PBSO) 算法.介绍对巴拉库达群优化 (PBSO) 算法.
- 解决高维优化的传统方法的局限性.
- 增强全球搜索能力,减轻当地最佳陷.
主要方法:
- 开发了配对巴拉库达群优化 (PBSO) 算法,具有独特的巴拉库达配对策略.
- 加入了一个支持barracuda来增强领先的对并改善全球搜索.
- 使用CEC2017标准测试函数评估PBSO性能.
主要成果:
- 与五个最先进的优化器相比,PBSO在29个测试功能中实现了卓越的性能.
- 获得了9个第一名,13个第二名和5个第三名,平均排名为2.0345.
- 在减轻维度灾难和局部优化方面证明有效.
结论:
- 双巴拉库达群优化 (PBSO) 算法是高维优化问题的高效解决方案.
- PBSO提供了一个可靠和优越的替代品,以现有的自然灵感优化器.
- 新的配对策略和支持巴拉库达增强复杂的搜索空间中的稳定性和准确性.
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