一个改进的Coati优化算法,为工程设计优化问题提供多种策略.
Zhang Qi1,2, Dong Yingjie3, Ye Shan4
1Chengdu Technological University, Chengdu, 611730, China.
Scientific reports
|September 3, 2024
概括
本研究介绍了TNTWCOA算法,通过改进初始解决方案,探索和利用来提高Coati优化算法性能. 改进的算法在优化任务中表现出卓越的融合速度和准确性.
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:
- 科蒂优化算法 (COA) 在后期优化方面面临局限性,包括人口多样性丧失和过早地趋同到局部最佳.
- 现有的COA因勘探和开采能力不足而扎,阻碍了其整体表现.
- 需要增强的优化算法来保持多样性,并逃避复杂问题的局部最佳.
研究的目的:
- 提出一个改进的Coati优化算法 (COA),称为TNTWCOA,解决原始COA的性能问题.
- 通过新的战略,提高 COA 的融合速度,准确性和稳定性.
- 验证TNTWCOA在基准函数和现实世界工程设计问题上的有效性.
主要方法:
- 引入一个混乱序列机制来初始化种群位置,确保更均的分布和更高质量的初始解决方案.
- 纳入非线性惯性权重因子,以平衡本地开发和全球勘探能力.
- 整合了适应性T分布变异策略,以增加人口多样性和逃避局部最佳,加上适应性搜索的警报更新机制.
主要成果:
- 与COA,ICOA,GJO,OOA,SCSO和SABO相比,TNTWCOA算法在29个IEEE CEC2017测试函数上显著提高了合速度和优化准确性.
- 在各种优化任务中观察到强大的性能,这表明了算法的可靠性.
- 在工程设计问题上,TNTWCOA表现出强大的解决方案优势,包括压力容器,梁,三条杆架,轮和减速器设计.
结论:
- 提议的TNTWCOA算法有效地克服了标准COA的局限性,在速度,准确性和稳定性方面提供了卓越的性能.
- 混沌初始化,非线性惯性重量,自适应T分布变化和警报更新机制的组合有助于增强勘探和开发.
- 在复杂的工程优化问题中,TNTWCOA具有显著的实际应用潜力.
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