关于在二次赋值问题上使用双倍随机矩阵模型.
Valentino Santucci1, Josu Ceberio2
1University for Foreigners of Perugia, Perugia, 06123, Italy valentino.santucci@unistrapg.it.
Evolutionary computation
|February 20, 2025
概括
双随机矩阵 (DSM) 提供了一个有效的方法来解决复杂的 permutation 问题,如二次赋值问题 (QAP). 实验表明,DSM在分配和订单任务的效率和计算性能方面优于其他模型.
科学领域:
- 组合优化的优化.
- 计算数学 计算数学 计算数学
背景情况:
- 换问题在组合优化中具有挑战性.
- 优化排列的现有方法在信息利用上有很大差异.
研究的目的:
- 在估计分布算法中提出和评估双倍随机矩阵 (DSM),以优化 permutation.
- 评估DSM与其他排列模型相比的有效性和效率.
主要方法:
- 使用DSM作为对排列的概率模型.
- 为代概率模型更新开发高效的学习和采样方案.
- 在标准的二次赋值问题 (QAP) 基准上进行实验.
主要成果:
- DSMs表现出优越的有效性和计算效率,超过其他四个对QAP的排列模型.
- 对于分配问题和排序问题,如线性排序问题 (LOP),DSM表现有前途.
结论:
- DSM是分配和排序问题的有价值的工具.
- DSM的潜在应用扩展到其他优化范式,如遗传算法和梯度搜索.
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