一种双适应性随机增强黑猩猩优化算法,用于火灾检测和多维问题解决
Ziyang Zhang1, Lingye Tan1, Diego Martín2
1School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Scientific reports
|December 29, 2024
概括
一个新的黑猩猩优化算法 (CHOA) 变体,TASR-CHOA,提高了融合速度,并避免了复杂问题的局部最佳值. 这种改进的算法在众多基准和现实世界的挑战中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
- 灵感来自大自然的算法
背景情况:
- 黑猩猩优化算法 (CHOA) 是一种以自然为灵感的元启发.
- 原始的CHOA面临着在多维优化中缓慢的融合和局部最佳的挑战.
- 解决这些局限性对于实际应用至关重要.
研究的目的:
- 提出一种新的CHOA变体,称为TASR-CHOA,以克服现有的局限性.
- 为了提高融合速度并改善优化中的勘探-开采平衡.
- 验证TASR-CHOA在各种基准和现实问题上的有效性.
主要方法:
- 通过整合随机方法和双重自适应权重机制,开发了TASR-CHOA.
- 在29个常规,10个IEEE CEC-06和30个IEEE CEC-BC基准函数上评估了TASR-CHOA.
- 使用统计测试将TASR-CHOA与4个分类和18个IEEE CEC-BC算法进行了比较.
主要成果:
- 在73个评估功能和工程问题中,TASR-CHOA取得了卓越的表现,在54个评估功能和工程问题中排名第一.
- 在多个情况下,证明了与SHADE和CMA-ES等最先进的算法相比的结果.
- 成功地将TASR-CHOA应用于使用深卷积神经网络的计算机辅助火灾检测任务.
结论:
- 在复杂的优化任务中,TASR-CHOA显著改进了原来的CHOA.
- 拟议的改进将导致更快的融合和更好的全球搜索能力.
- TASR-CHOA为各种科学和工程应用提供了强大而有效的优化工具.
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