一个基于权力方法的新型超级隐喻元启发算法
Huiying Zhang1, Hanshuo Wu2, Yifei Gong2
1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, Jilin, China. yingzi1313@163.com.
Scientific reports
|July 24, 2025
概括
一个新的功率方法算法 (PMA) 通过模拟自身值计算来解决复杂的优化问题. 这种新的方法在基准和现实世界的工程挑战上展示了与现有方法相比的卓越性能.
科学领域:
- 计算数学 计算数学 计算数学
- 优化理论 优化理论
- 算法开发 算法开发
背景情况:
- 在大型稀疏矩阵中,复杂的优化问题和固有值问题带来了重大的计算挑战.
- 现有的元启发算法经常在平衡探索和利用方面扎,导致局部最佳.
- 功率代方法为开发新型优化策略提供了基础.
研究的目的:
- 介绍功率方法算法 (PMA),一种由功率代方法启发的新型元启发算法.
- 评估PMA在基准函数和现实世界的工程优化问题上的性能.
- 为了证明PMA在解决复杂的优化任务方面的有效性,以及在勘探和开采之间的平衡.
主要方法:
- 功率方法算法 (PMA) 模拟了主导的自值和自向量计算过程.
- PMA 结合了随机角度生成和调整因子,以提高解决问题的能力.
- 该算法在CEC 2017和CEC 2022测试套件中的49个基准函数和8个现实工程问题上进行了严格的测试.
主要成果:
- 在各种维度中,PMA超过了九个最先进的元启发算法,由低平均弗里德曼排名 (3,2.71,2.69) 证明.
- 统计测试 (威尔科克森等级和,弗里德曼) 证实了PMA的稳定性和可靠性.
- 对于现实世界的工程优化问题,PMA始终提供最佳解决方案,展示了高合效率和避免局部最佳.
结论:
- 动力方法算法 (PMA) 是一个具有竞争力和实际价值的元启发法,用于跨学科复杂的优化任务.
- 普马有效平衡勘探和开采,确保有效的融合和最佳解决方案的发现.
- 该算法的基准和现实世界问题的性能突出显示了其在大型稀疏矩阵中解决自身值问题的潜力.
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