CMGWO:用于类似于聚变电池的P系统的灰狼优化器
Yourui Huang1, Quanzeng Liu1, Hongping Song1
1Anhui University of Science and Technology, Huainan, 232001, China.
Heliyon
|August 8, 2024
概括
这项研究介绍了一种新的灰色狼优化器,增强了与融合细胞类似的P系统. 这种改进的算法克服了局部最佳,实现了更快的融合和更好的稳定性在优化任务.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 生物启发的计算 生物启发的计算
背景情况:
- 灰狼优化器 (GWO) 是一个流行的元启发算法,但存在过早的融合和局部优化问题.
- 类似细胞的P系统提供并行计算和细胞间通信,为增强群集智能算法提供了一个框架.
研究的目的:
- 提出一个改进的灰狼优化器,使用类似于聚变电池的P系统来提高全球搜索能力.
- 解决传统GWO的局限性,特别是它倾向于陷入局部最佳化的局限性.
- 在各种优化问题上评估拟议的算法的性能.
主要方法:
- 融合细胞类P系统与灰狼优化器的集成.
- 设计新的收因子和动态权重,以平衡勘探和开发.
- 在P系统框架内并行计算和细胞间通信机制.
- 测试24个基准函数,支持矢量机参数优化和受约束的工程设计问题.
主要成果:
- 拟用于类似聚变电池的P系统的灰狼优化器在传统算法上表现出卓越的性能.
- 实现了更高的精度,更快的收速度,并提高了测试函数的稳定性.
- 在六个基准数据集中成功优化了支持向量机参数.
- 在三个现实世界受约束的工程设计问题上显示了竞争性结果.
结论:
- 类似于融合细胞的P系统有效地增强了灰狼优化器逃避局部优化的能力.
- 修改后的算法显示了改善的人口搜索能力,更快的融合和更大的稳定性.
- 提出的方法为开发更强大,更有效的优化技术提供了一个有希望的方向.
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