一个新的以人为基础的元启发算法,用于解决基于技术和职业教育和培训的优化问题
Marie Hubalovska1, Stepan Major1
1Department of Technics, Faculty of Education, University of Hradec Kralove, CZ50003 Hradec Kralove, Czech Republic.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
一个新的基于技术和职业教育和培训的优化算法 (TVETBO) 有效地解决了复杂的优化问题. 与现有方法相比,TVETBO在基准和现实应用中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 优化问题在科学和工程学科中普遍存在.
- 现有的元启发算法在平衡探索和开发方面面临着挑战.
- 需要一种以职业培训为灵感的新方法来增强优化能力.
研究的目的:
- 引入一种新的基于人类的元启发算法,即基于技术和职业教育和培训的优化器 (TVETBO).
- 基于教育阶段的数学模型TVETBO:理论,实践和技能发展.
- 评估TVETBO在基准和现实世界的受约束优化问题上的表现.
主要方法:
- 开发了基于技术和职业教育和培训的优化算法 (TVETBO).
- 在各种维度 (10,30,50,100) 上使用CEC 2017基准套件测试了TVETBO.
- 将TVETBO与12个已建立的元启发算法进行比较,并将其应用于CEC 2011的受约束问题.
主要成果:
- 在基准功能上,TVETBO表现出强大的勘探,开发和平衡能力.
- 在大多数CEC 2017基准函数上,TVETBO的表现优于12个竞争算法.
- TVETBO在CEC 2011真实世界的受约束优化问题上表现出卓越的表现.
结论:
- 提议的TVETBO算法对于解决复杂的优化任务是有效的.
- 电视ETBO为现有的元启发方法提供了一个有前途的替代方案.
- 电视ETBO显示了对现实世界应用程序优化的巨大潜力.
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