基于自适应螺旋飞行和多策略融合的FOX优化算法
Zheng Zhang1, Xiangkun Wang2, Li Cao2
1School of Information Engineering, Wenzhou Business College, Wenzhou 325035, China.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
一个新的FOX优化算法使用自适应螺旋飞行和多策略融合来克服局部最佳等局限性. 这种增强的方法在测试中显示了更好的融合速度,准确性和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 群集情报 群集情报 群集情报
背景情况:
- 原始的FOX优化算法具有较弱的ergodicity,低多样性,并倾向于卡在局部最佳状态中.
- 提高群集智能算法对于解决复杂的优化问题至关重要.
- 初始化策略显著影响算法性能.
研究的目的:
- 开发一个增强的FOX优化算法,解决原始方法的局限性.
- 改进FOX算法的勘探和开发能力.
- 在基准函数和工程问题上评估增强算法的性能.
主要方法:
- 使用帐混乱地图进行初始化,以提高 ergodicity 和多样性.
- 集成惯性重量以提高溶液质量.
- 适应性随机步行的可变螺旋位置更新策略.
- 纳入征税航班和贪的方法,以实现全球和本地搜索的平衡.
- 多种策略的融合,以提高优化效果.
主要成果:
- 改进的FOX算法与其他群集智能算法相比,显示出更高的融合速度.
- 观察到溶液精度和稳定性的显著改善.
- 该算法显示出更强的能力逃脱局部最佳.
- 在CEC2017基准测试函数和工程应用优化问题上的有效性能.
结论:
- 增强的FOX优化算法有效地克服了原始方法的缺点.
- 多策略融合方法导致优化性能取得了显著的进步.
- 升级的算法为复杂的优化任务提供了强大而高效的解决方案.
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