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增强的鱼优化算法:用于机器人手臂轨迹规划的正交折射对立式基于学习
Yuefeng Leng1, Chunlai Cui1, Zhichao Jiang1
1School of Mechanical Engineering, Liaoning Technical University, Fuxin, China.
PloS one
|February 5, 2025
概括
增强的鱼优化算法 (ECOA) 通过增强全球搜索和避免局部优化来改善机器人手臂轨迹规划. 这种元启发式方法为复杂的优化任务提供了卓越的性能和稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算优化计算优化
背景情况:
- 在高维空间中的轨迹规划在计算上是复杂的.
- 超启发式 (MH) 算法提供了实际的解决方案.
- 鱼优化算法 (COA) 在全球搜索和本地最佳趋同方面存在局限性.
研究的目的:
- 为机器人手臂轨迹规划提出一个增强的鱼优化算法 (ECOA).
- 提高全球搜索能力和趋同稳定性.
- 为了提高解决方案的质量和搜索效率.
主要方法:
- 整合了一个帐混乱地图用于人口初始化.
- 用非线性扰动因子取代传统的步骤尺寸调整.
- 实施了基于对立的正交折射反射学习策略.
主要成果:
- 在轨道规划实验中,ECOA表现优越.
- 与最好的竞争算法相比,成本降低了15%,与COA相比降低了10%.
- 展示了改进的解决方案质量,稳定性和趋同稳定性.
结论:
- ECOA有效地解决了原来的COA的局限性.
- 提出的策略可以提高优化性能.
- ECOA显示了各种工程优化挑战的潜力.
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