一个强化鱼优化算法用于解决数学优化问题
Yunpeng Ma1, Xiaolu Wang2, Wanting Meng2
1School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
强化鱼优化算法 (RWOA) 提高了融合速度和解决方案质量. 与标准鱼优化算法相比,这种改进的算法在基准测试函数上显示出更高的性能.
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:
- 鱼优化算法 (WOA) 是一个受欢迎的元启发法,以其简单性和逃避局部优化的能力而闻名.
- 然而,在融合速度和解决方案质量方面的局限性要求对复杂的优化任务进行进一步改进.
研究的目的:
- 引入一个强化的鱼优化算法 (RWOA),旨在提高融合速度和解决方案质量.
- 在实际优化场景中解决标准WOA的局限性.
主要方法:
- 整合基于对立的学习,以增加解决方案多样性和加速融合.
- 引入了一个动态适应系数,以平衡勘探和开发阶段.
- 在围绕猎物阶段实施了个人信息增强机制,以提高解决方案的质量.
主要成果:
- 与WOA相比,RWOA在20个基准,21个CEC-2017和8个CEC-2022测试函数中表现出卓越的融合精度和稳定性.
- 威尔科克森的等级总和测试证实了与其他算法相比,RWOA的统计学上显著改善.
结论:
- RWOA有效地改进了标准WOA,在优化任务中提供了更高的性能.
- 拟议的改进为各种应用提供了更强大,更有效的优化工具.
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