快速飞行优化器 (Swift Flight Optimizer):一种基于快速鸟类行为的生物灵感优化算法
Abbas Aqeel Kareem1, Ahmed Jabbar Abid1, Dalal Abdulmohsin Hammood1
1Electrical Engineering Technical College, Middle Technical University, Baghdad, 10001, Iraq.
Scientific reports
|November 25, 2025
概括
一个新的生物灵感算法,即Swift Flight Optimizer (SFO),有效地解决了复杂的优化问题. 与现有方法相比,SFO在速度和解决方案质量方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 生物启发的计算 生物启发的计算
背景情况:
- 对复杂,高维度优化而言,元启发式算法至关重要.
- 现有的方法经常受到过早的融合和不良的勘探-开发平衡的影响.
- 需要新的算法来克服这些局限性.
研究的目的:
- 介绍Swift Flight Optimizer (SFO),这是一个新的生物灵感算法.
- 在解决具有挑战性的优化问题时解决当前元启发术的局限性.
- 评估SFO在基准功能上的表现.
主要方法:
- 基于快速鸟类适应性飞行动力学的SFO开发.
- 实施了多模式框架:滑翔 (勘探),目标 (开发),微 (提炼).
- 纳入了意识到停滞的重新启动策略,以保持人口多样性.
主要成果:
- 在CEC2017基准函数21/30 (10D) 和11/30 (100D) 上,SFO获得了最佳平均健身.
- 证明了加速融合和平衡的勘探开发.
- 在速度,质量和稳定性方面超过了13个最先进的优化器 (PSO,GWO,WOA,EMBGO).
结论:
- SFO是一种新且具有竞争力的元启发.
- 它显示了大规模,多式联运,高维优化的巨大潜力.
- SFO提供了持续的人口多样性,并有效地减轻了过早的融合.
关键词:
生物启发的优化优化勘探开采余额 勘探开采余额在IEEE CEC2017的基准指标.超启发式算法 (Metaheuristic Algorithms) 是一种算法,可以通过粒子群集优化 (PSO) 是一种避免停滞,避免停滞.快速飞行优化器 (SFO) 快速飞行优化器更多相关视频
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