一个rhinopithecus群群优化算法用于复杂的优化问题
Guoyuan Zhou1, Dong Wang1, Guoao Zhou2
1College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
Scientific reports
|July 7, 2024
概括
一个新的Rhinopithecus Swarm Optimization (RSO) 算法有效地解决了高维问题. 灵感来自于鼻动物的行为,RSO在基准测试和工程应用中显示出与现有方法相比更高的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 高维优化问题在各种科学和工程领域都带来了重大挑战.
- 现有的元启发式算法经常在高维搜索空间中的可扩展性和效率方面扎.
- 需要新的方法来提高复杂问题的优化技术的性能.
研究的目的:
- 介绍一个新的元启发算法,Rhinopithecus Swarm Optimization (RSO),旨在解决高维优化问题.
- 通过将其性能与已建立的优化算法进行比较来研究RSO的有效性.
- 为了证明RSO在解决复杂的工程设计问题中的实际应用性.
主要方法:
- 提出了Rhinopithecus Swarm Optimization (RSO) 算法,其灵感来源于rhinopithecus群的社会结构和行为.
- RSO将个人分为成熟,青少年和婴儿阶段,每个人都采用不同的搜索策略:垂直迁移,协同搜索和模仿.
- 性能评估涉及CEC2017测试套件和三个受约束的工程问题,并使用Wilcoxon签名等级和弗里德曼测试进行了广泛的统计分析.
主要成果:
- 在CEC2017测试中,RSO在30维和100维测试中表现出色,获得第一名.
- RSO的表现优于八种众所周知的优化算法,包括DBO,BWO,SSA,AVOA,WOA,ARBBPSO,GTO和HHO.
- 该算法在评估的三个经典工程设计问题中始终产生了最佳结果.
结论:
- 犀牛群优化 (RSO) 是一种高效的算法,用于解决高维优化挑战.
- 在RSO内部独特的分工和搜索策略有助于其卓越的性能.
- RSO为处理复杂优化任务的研究人员和工程师提供了一个有前途的新工具.
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