OOBO:一种用于解决优化问题的新元启发算法
Mohammad Dehghani1, Eva Trojovská1, Pavel Trojovský1
1Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
一种新的优化技术,即一对一基础优化器 (OOBO),通过让所有成员参与人口更新来提高算法性能. 这种方法在复杂的优化和工程问题中取得了卓越的结果.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 应用数学 应用数学 应用数学
背景情况:
- 优化问题在科学学科中普遍存在.
- 现有的算法可能在人口成员参与和勘探-开采平衡方面存在局限性.
- 需要新的优化技术来应对复杂的现实世界挑战.
研究的目的:
- 介绍一对一基础优化器 (OOBO),一种新的元启发优化算法.
- 加强所有人口成员参与优化过程的参与.
- 在优化算法中改善勘探和开发之间的平衡.
主要方法:
- 开发了一种基于一对一的优化器 (OOBO),利用一对一对应来进行人口更新.
- 评估了CEC 2017测试套件中的52个目标函数的OOBO性能 (单模,多模,高维).
- 与八个已建立的优化算法进行OOBO比较.
主要成果:
- OOBO展示了在平衡勘探和开采方面的卓越能力.
- 与现有的算法相比,拟议的优化器实现了更可接受的准最佳解决方案.
- 在解决各种优化问题方面,OOBO的表现优于其他算法.
结论:
- 一对一基础优化器 (OOBO) 是一种有效的新技术,用于各种优化任务.
- 在解决复杂的科学和工程优化问题方面,OOBO显示出显著的前景.
- 算法的独特更新机制有助于提高其性能和适用性.
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