采用动态接触式飞行和精英指导策略的增强雪除除优化器
Guoping You1, Yudan Hu1, Zhen Yang1
1School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China.
Scientific reports
|March 24, 2025
概括
增强的雪除优化器 (ESAO) 通过结合混乱映射和自适应策略,改进了原来的SAO. 在优化任务中,ESAO表现出卓越的性能,在速度,稳定性和准确性方面超过了其他11个算法.
科学领域:
- 优化算法的优化算法
- 超启发式计算是一种超启发式计算.
- 计算智能是一种计算智能.
背景情况:
- 雪除优化器 (SAO) 是一种新的元启发算法.
- 在SAO中,局部最佳陷,缓慢的融合,以及复杂的多式联运功能的表现不佳.
研究的目的:
- 引入增强的雪除除优化器 (ESAO).
- 解决原来的SAO算法的局限性.
- 评估ESAO在基准功能和现实问题上的有效性.
主要方法:
- 实现了ESAO,具有混乱映射,随机对立学习初始化,动态触点飞行,自适应惯性重量和精英引导边界控制.
- 在29个CEC2017基准函数,19个CEC2020工程挑战和无人机飞行轨迹优化上测试了ESAO.
- 将ESAO与经典的,最近的和变种算法进行比较 (PSO,HHO,GWO,GOOSE,HEOA,Puma,SAO,IGWO,IDBO,HPHHO,E-WOA). 它们的比较结果是:
主要成果:
- 与11个竞争算法相比,ESAO在各种测试场景中展示了优越的融合速度,稳定性和准确性.
- 统计测试 (弗里德曼和威尔科克森) 证实了ESAO显著的性能优势.
- 欧空局有效优化了无人机飞行轨迹.
结论:
- ESAO克服了SAO的局限性,提供了增强的优化功能.
- 作为一个强大的元启发算法,ESAO显示出重要的潜力,用于各种优化挑战.
- 提议的改进显著提高了融合和解决方案的质量.
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