多策略改进的鱼优化算法用于数值优化和无人机路径规划
Lu Li1, Haonan Zhao2, Lixin Lyu1
1School of Information and Artificial Intelligence, Anhui Business College, Anhui, 241002, China.
Scientific reports
|April 23, 2025
概括
多策略改进的加优化算法 (MIGOA) 通过提高融合速度和精度来增强原来的加优化算法 (GOA). MIGOA有效地解决了复杂的优化问题,包括无人机路径规划.
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:
- 盖塞尔优化算法 (GOA) 是一个公认的元启发式,但它面临着缓慢的融合和局部优化的挑战.
- 实际应用需要优化算法,以提高精度和更快的融合率.
研究的目的:
- 引入多策略改进的加塞尔优化算法 (MIGOA),以克服标准GOA的局限性.
- 提高 Gazelle 优化算法的勘探能力,融合速度和精度.
主要方法:
- 开发了MIGOA,使用最佳点集,触点飞行搜索,适应性步骤大小和新的探索策略进行人口初始化.
- 评估了MIGOA在CEC2017和CEC2020的基准测试集与GOA和其他八个算法.
- 应用MIGOA到3D无人机路径规划和两个工程设计问题.
主要成果:
- 在CEC2017和CEC2020基准函数上,MIGOA表现出色,平均排名很好.
- 使用威尔科克森等级和弗里德曼平均等级测试的统计验证证了MIGOA的有效性.
- 在无人机路径规划和工程设计中,MIGOA成功解决了受约束的优化问题.
结论:
- 拟议的MIGOA显著改善了标准的GOA,提供了增强的勘探,更快的融合和更高的精度.
- MIGOA显示出强大的可扩展性和实际适用性,用于解决复杂的,现实世界的优化挑战.
- 增强的算法有效地避免了局部优化,使其成为各种优化任务的强大工具.
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