多任务蛇优化算法用于全局优化和平面运动臂控制问题
Qingrui Li1, Yongquan Zhou1,2, Qifang Luo1,2
1College of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi, China.
PeerJ. Computer science
|March 10, 2025
概括
本研究引入了一种新的多任务优化 (MTO) 算法,即多任务蛇优化 (MTSO),以提高精度和降低计算成本. 通过在任务之间智能转移知识,MTSO提高了各种复杂问题的性能.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 现有的多任务优化 (MTO) 算法面临有限精度和高计算需求的挑战.
- 需要更高效,更准确的MTO方法来解决复杂,相互关联的问题.
研究的目的:
- 提出一种新的多任务蛇优化 (MTSO) 算法.
- 解决现有的MTO方法的局限性,特别是提高优化精度和降低计算成本.
主要方法:
- 拟议的MTSO算法采用了两阶段的方法:独立处理问题和知识转移.
- 知识转移是由转移和精英个人选择的概率决定的,这使得精英能够跨任务共享知识或自我扰动任务更新.
主要成果:
- 与高级MTO算法相比,MTSO算法在多任务基准函数上的精度更高.
- 在复杂的应用中观察到显著的改进,包括平面动力学手臂控制,机器人抓手设计和汽车侧撞设计问题.
结论:
- 对于多任务优化问题,MTSO算法提供了更准确,更高效的计算解决方案.
- 其有效的知识传输机制有助于在多样化和具有挑战性的工程和计算任务中提高性能.
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