使用进化的多目标算法优化单调的机会受约束的子模块函数
Aneta Neumann1, Frank Neumann2
1Optimisation and Logistics, The University of Adelaide, Adelaide, Australia aneta.neumann@adelaide.edu.au.
Evolutionary computation
|September 24, 2024
概括
进化的多目标算法在机会受约束的亚模块化优化问题上表现得更好. 这些算法,包括GSEMO,NSGA-II和SPEA2,在复杂的网络场景中优于贪的方法.
科学领域:
- 优化优化 优化优化
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 现实世界的优化问题经常利用子模块函数.
- 这些问题的不确定性可能导致约束违规.
- 进化型多目标算法 (EMOA) 越来越多地应用于受约束的分模块问题.
研究的目的:
- 为了呈现第一个运行时分析的EMOAs的机会受约束的子模块函数.
- 在概率约束下调查GSEMO算法的性能.
- 在子模块化网络优化任务中将EMOA与贪算法进行比较.
主要方法:
- 对双目标配方的GSEMO算法的运行时分析.
- 使用尾部边界来评估在机会限制下解决方案的可行性 (概率α).
- 关于子模块网络问题的GSEMO,NSGA-II和SPEA2的实验评估.
主要成果:
- 在特定的重量分布下,GSEMO在单调子模块函数的贪算法中实现了与贪算法相比较的最坏情况下的性能保证.
- 一个配方的尾部界限可能会阻碍GSEMO在非单调子模块函数的性能.
- 在实验性亚模块化机会受约束网络问题中,EMOA在贪算法上显示出显著的性能增长.
结论:
- EMOA提供了一个强大的方法来解决具有概率约束的亚模块化优化.
- 选择双目标表述和处理单调性对于算法性能至关重要.
- 对于复杂,不确定的优化挑战,EMOA比传统的贪方法更有前途.
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