一个稳定状态的微基因算法,用于在一个维的垃圾包装箱中进行超启发式生成
Julio Juárez1, Jesús Guillermo Falcón-Cardona1, José Carlos Ortiz-Bayliss2
1School of Engineering and Sciences, Tecnologico de Monterrey, 64700, Monterrey, Mexico.
Scientific reports
|July 27, 2025
概括
这项研究引入了一种新的方法,用于自动生成选择超启发式 (HHs),以解决一维垃圾装填问题 (1DBPP). 拟议的稳态μ遗传算法 (SSμGA) 有效地创建了具有竞争力的HH,这些HH显示了专业化和泛化能力.
科学领域:
- 计算机科学 计算机科学
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 一维垃圾包装问题 (1DBPP) 是一个具有重大现实应用的NP硬优化挑战.
- 对于大型1DBPP实例来说,准确的算法通常是不可行的,因此需要使用启发式.
- 现有的启发式可以是特定于实例的,在不同的问题类型中执行不一致.
研究的目的:
- 为1DBPP开发一种自动生成有效选择超启发式 (HHs) 的方法.
- 利用简单启发式的优势,同时减轻其个体弱点.
- 提高解决1DBPP的算法的性能和适应性.
主要方法:
- 引入一个稳定状态μ遗传算法 (SSμGA) 来产生选择HHs.
- 使用稳定状态GA的逐渐人群更新和小人群的μGA的效率.
- 与其他进化生成方法比较的比较分析,如代 GA,稳定状态 GA和代 μGA.
主要成果:
- 与同一个评估预算内的其他进化方法相比,SSμGA在培训套件上始终实现了更高的适应性值.
- 由SSμGA生成的选择HH在测试集中的生成和文献1DBPP实例中表现出高竞争力.
- 由SSμGA生成的HH表现出针对特定实例类型的专业化和跨不同实例的概括性的平衡.
结论:
- SSμGA是一种有效的方法,可以自动生成1DBPP的高性能选择超启发式.
- 产生的HHs比其他进化技术产生的方法提供了更好的性能和适应性.
- 这项工作为解决复杂的组合优化问题 (如1DBPP) 提供了一个强大的方法.
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