在全球优化问题中应用一种由体育场观众启发的新型元启发算法.
Mehrdad Nemati1, Yousef Zandi2, Alireza Sadighi Agdas1
1Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Scientific reports
|February 6, 2024
概括
开发了一种新的无参数优化算法,即体育场观众优化器 (SSO). 它在许多测试函数上显示了与现有方法相比较的性能,为复杂的优化问题提供了更简单的方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 超启发式算法通常需要广泛的参数调整以获得最佳性能.
- 需要无参数优化方法,可以有效地解决各种问题.
- 新型算法对于推进计算智能和解决问题的能力至关重要.
研究的目的:
- 介绍一个新的无参数元启发算法,即体育场观众优化器 (SSO).
- 在数学上建模和评估SSO算法的性能和效率.
- 将SSO算法与基准函数的既定优化技术进行比较.
主要方法:
- 开发了体育场观众优化器 (SSO) 算法,灵感来自人群行为.
- 数学建模和SSO算法的实现.
- 使用标准数学测试函数和CEC-BC-2017各种维度的基准套件进行绩效评估.
主要成果:
- 该SSO算法展示了无参数优化,消除了额外参数设置的需要.
- 在14个数学测试函数上,SSO使用最先进的技术显示了可比且强大的性能.
- 虽然EBOwithCMAR在一些CEC-BC-2017功能上表现优于SSO,但SSO排名第二,表现优于CMA-ES,这表明了竞争性表现.
结论:
- SSO算法提供了一个可行的无参数的优化方法,简化了应用.
- 对于已建立的算法来说,SSO表现出具有竞争力的性能,特别是其在没有参数调整的情况下解决问题的能力.
- 进一步的研究可能会探索在更高的维度和更复杂的现实世界优化任务中提高SSO的性能.
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