一个集成的TLBO驱动的混合灰狼优化器,用于高效地解决多维的非线性工程问题.
Harleenpal Singh1, Sobhit Saxena1, Himanshu Sharma2
1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India.
Scientific reports
|April 1, 2025
概括
一个新的混合优化算法,灰狼优化器-教学基于学习的优化 (GWO-TLBO),提高了解决方案的准确性. 这种新的方法平衡了探索和利用,在复杂的优化问题中获得可靠的结果.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 算法设计 算法设计
背景情况:
- 灰狼优化器 (GWO) 在探索解决方案方面表现出色,但在微调过程中,它在过早地趋同到低于最佳的结果方面扎.
- 基于教学学习的优化 (TLBO) 通过模拟教育过程有效地提高了搜索能力.
- 混合化是克服个别元启发算法的局限性的关键策略.
研究的目的:
- 介绍和评估一种新的混合优化算法,GWO-TLBO.
- 通过整合基于教学学习的优化来解决灰狼优化器的微调弱点.
- 为了提高整体搜索能力和优化算法的融合准确性.
主要方法:
- 灰狼优化器-教学基于学习的优化 (GWO-TLBO) 混合算法的开发.
- 将基于教学优化 (TLBO) 原则纳入灰狼优化器 (GWO) 框架.
- 在各种基准优化问题上应用和测试GWO-TLBO算法.
主要成果:
- 在不同复杂度的基准优化问题中,GWO-TLBO算法表现出卓越的性能.
- 对比分析显示,GWO-TLBO比现有的优化算法更快,更准确,更可靠.
- 混合方法有效地平衡了勘探和开采,改善了接近全球最佳的识别.
结论:
- 对于具有挑战性的优化任务,GWO-TLBO提供了强大可靠的解决方案.
- 整合TLBO显著提高了GWO的开发和微调能力.
- 这种新的混合算法在元启发优化领域呈现出有前途的进步.
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