整合了MULTIMOORA算法与圆形q-rung整形pair模糊信息相结合,以优化玩家定位
Asma Farhad1, Kifayat Ullah2,3, Zeeshan Ali4
1Department of Mathematics, Riphah International University (Lahore Campus), Lahore, 54000, Pakistan.
Scientific reports
|October 16, 2025
概括
这项研究引入了一个新的框架,用于优化玩家的定位,使用多目标优化通过比率分析与完整的乘法形式 (MULTIMOORA). 开发的圆形q-rung整形pair模糊集 (Cq-ROFSs) Dombi操作员在复杂的场景中提高决策准确性.
科学领域:
- 运营研究 运营研究
- 决策科学 决策科学 决策科学
- 模糊的数学 模糊的数学
背景情况:
- 玩家定位优化对于性能至关重要,但由于多个非可比变量而具有挑战性.
- 现有的决策框架可能无法充分处理复杂的不确定性和动态参数.
研究的目的:
- 为优化玩家定位提出一个新的分析框架.
- 通过比率分析与完整的乘法形式 (MULTIMOORA) 集成多目标优化与先进的模糊集合理论.
- 开发新的Dombi优先聚合运算符,基于循环q-rung整形pair模糊集 (Cq-ROFSs).
主要方法:
- 实施MULTIMOORA方法进行决策.
- 使用Dombi优先权加权聚合运算符实现框架的运行.
- 开发和应用循环 q-rung orthopair 模糊集 (Cq-ROFSs) 用于多维不确定性表示.
- 引入了两个新型运算符:Cq-ROFSs Dombi优先权加权平均 (Cq-ROFSDPWA) 和Cq-ROFSs Dombi优先权加权几何 (Cq-ROFSDPWG) 运算符.
主要成果:
- 经验实验验证了拟议方法的性能和实际相关性.
- 开发的Cq-ROFS有效地代表了多维不确定性,并允许动态参数调整.
- 数字示例证实了聚合运营商在各种决策场景中的逻辑和一致结果.
结论:
- 拟议的分析框架和新的Cq-ROFSs Dombi优先级运营商显著提高了多目标决策,特别是在玩家定位优化方面.
- 与现有方法相比,Cq-ROFSDPWA和Cq-ROFSDPWG运营商表现出优异的性能.
- 该方法为具有不确定的参数的复杂优化问题提供了强大的和可适应的工具.
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