在多标准决策中,修复不一致的双对比矩阵,使用共弦距离和基于灰狼优化器的框架
Shalu Kaushik1, Sangeeta Pant2, Lokesh Kumar Joshi1
1Department of Applied Science (Mathematics), Gurukula Kangri (Deemed to Be University), Haridwar, India.
这项研究引入了一种新方法,使用直线距离和灰狼优化器 (GWO) 来修复决策中不一致的对对比矩阵 (PCM). 这种方法确保了对原始判断的最小改变的一致性.
科学领域:
- 决策科学 决策科学
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 不一致的对对比矩阵 (PCM) 阻碍了可信的多标准决策 (MCDM).
- 优化PCM需要尽量减少与原始判断的偏差,同时提高一致性比率 (CR).
研究的目的:
- 开发一个新的框架来检测和纠正PCM中的不一致性.
- 通过一致的矩阵生成,提高决策过程的可信度.
主要方法:
- 利用基于等号距离的新型距离公式来量化PCM不一致性.
- 使用灰狼优化器 (GWO),一个群集智能算法,用于矩阵修复.
- 引入了最大校正范围 (ε),以控制基于决策者偏好的矩阵调整的程度.
主要成果:
- 在一个特殊案例矩阵中,CR从0.546487显著降低到0.073397.
- 证明成功生成了一个一致的矩阵,与原始的最小偏差.
- 与ANTAHP和PSO等现有算法相比,它展示了卓越的性能.
结论:
- 拟议的框架有效地解决了PCM不一致性,使用共弦距离和GWO.
- 该方法保留了决策者的偏好,同时实现了高度的一致性.
- 这种方法为提高MCDM的可靠性提供了一个强大的解决方案.
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