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纳入PSO的混合人工蜂鸟算法与精英的基于对立的学习和考奇突变:对CSGC-球曲线的形状优化的案例研究
Kang Chen1, Liuxin Chen2,3, Gang Hu3,4
1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
一个新的混合人工蜂鸟算法 (HAHA) 优化了复杂的复合形状可调整的通用立方球 (CSGC-Ball) 曲线. 这种增强的算法提高了对汇率的准确性,并避免了局部优化以获得更好的几何建模.
科学领域:
- 计算机辅助的几何设计 (CAGD)
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 几何建模和复杂的曲线设计在计算机技术中至关重要.
- 现有的元启发式算法,如人工蜂鸟算法 (AHA),在汇聚精度方面存在局限性,并避免了局部最佳.
- 在工程应用中,需要先进的算法来优化复杂的,可调整的曲线.
研究的目的:
- 开发和评估混合人工蜂鸟算法 (HAHA) 以优化复杂的复合形状可调整的概括立方球 (CSGC-Ball) 曲线.
- 通过结合精英的基于对立的学习,粒子集群优化 (PSO) 和考希突变来提高人工蜂鸟算法 (AHA) 的性能.
- 验证HAHA在为CSGC-Ball曲线实现准确和高效的形状优化的有效性.
主要方法:
- 拟议的混合人工蜂鸟算法 (HAHA) 将精英的基于对立的学习,PSO和Cauchy突变与原来的AHA集成在一起.
- 使用SGC-Ball基础函数构建CSGC-Ball曲线,允许通过形状参数进行全球和本地形状调整.
- 为CSGC-Ball曲线建立了一个形状优化模型,该模型基于最小化曲线能量,并使用HAHA解决.
主要成果:
- 与其他先进算法相比,HAHA在基准测试函数和CEC 2022测试套件上表现出卓越的性能.
- 使用弗里德曼和威尔克森等级总和测试的统计分析证实了HAHA的竞争力和实用性.
- 拟议的HAHA有效地解决了CSGC-Ball曲线形状优化问题,通过数值示例进行验证.
结论:
- 混合人造蜂鸟算法 (HAHA) 在融合准确度方面提供了显著的改进,并避免了对元启发算法的本地优化.
- CSGC-Ball曲线为工程中的复杂曲线建模提供了灵活的全球和本地形状调节能力.
- 哈哈是解决CSGC-Ball曲线形状优化问题的高效和优质方法,推进几何建模能力.
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