一个多策略改进的树种算法用于数值优化和工程优化问题
Jingsen Liu1,2, Yanlin Hou1,2, Yu Li3,4
1International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, China.
Scientific reports
|July 4, 2023
概括
本研究介绍了一种改进的树种算法 (PDSTSA),用于连续优化问题. PDSTSA增强了融合速度和优化准确性,在模拟和现实世界工程测试中超越现有的算法.
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:
- 连续优化问题带来了诸如局部优化和缓慢收等挑战.
- 现有的树种算法 (TSA) 在勘探和开发方面存在局限性.
- 需要强大的算法来解决复杂的优化任务至关重要.
研究的目的:
- 提出一个增强的树种算法 (PDSTSA),以解决TSA的局限性.
- 改善全球搜索能力和人口多样性.
- 在基准和工程问题上验证PDSTSA的有效性.
主要方法:
- 整合了一个模式搜索策略,以加强全球检测.
- 引入一个维数变异突变策略,以保持人口多样性.
- 实施一次性改进的消除和更新机制.
主要成果:
- 与IEEE CEC2015测试函数的其他七种算法相比,PDSTSA展示了优越的优化准确性和融合速度.
- 威尔科克森等级总和测试证实了统计学上显著的绩效差异.
- PDSTSA在工程受约束优化问题上被证明是有效和优越的.
结论:
- 拟议的PDSTSA有效地克服了传统的TSA的局限性.
- PDSTSA为持续和受约束优化提供了强大而高效的解决方案.
- 该算法显示了工程领域及其他领域实际应用的巨大潜力.
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