MOBRO:多目标的皇家战斗优化器
Sait Alp1, Rahim Dehkharghani2, Taymaz Akan3,4
1Department of Computer Engineering, Erzurum Technical University, Erzurum, Turkey.
概括
新的多目标战斗皇家优化器 (MOBRO) 有效地解决复杂的多目标问题. 这种基于游戏的优化算法与基准数据集上的现有方法相比,显示出更高的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 战斗皇家优化器 (BRO) 是一种基于游戏的优化算法.
- 现有的BRO版本解决了单一目标的问题,留下了一个多目标应用程序的空白.
- 没有免费午餐定理强调了对多样化的优化算法的需求.
研究的目的:
- 开发和实施一个多目标版本的战斗皇家优化器 (MOBRO).
- 评估MOBRO在标准多目标基准数据集上的表现.
- 将MOBRO与最先进的多目标优化算法进行比较.
主要方法:
- 拟议的多目标战斗皇家优化器 (MOBRO) 被设计和实施.
- MOBRO被应用于CEC 2009,CEC 2018,ZDT和DTLZ基准数据集.
- 通过反向代际距离,最大传播和间隔指标来评估绩效.
主要成果:
- 在大多数基准套件中,MOBRO表现出卓越的性能.
- 该算法显示了与其他最先进的方法相比具有竞争力的结果.
- 评估侧重于优化的一致性,扩散和分布方面.
结论:
- MOBRO成功地将基于游戏的优化方法扩展到多目标问题的领域.
- 开发的算法为现有的多目标优化技术提供了有竞争力的替代方案.
- 进一步的研究可以探索MOBRO在各种科学和工程领域的应用.
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