一个新的多策略改进了准对立的混沌衣群算法,用于全球优化和受约束的工程应用程序.
Vanisree Chandran1, Prabhujit Mohapatra1
1Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Heliyon
|May 23, 2024
概括
一个新的准对立的混沌衣群算法 (QOCTSA) 通过结合准对立的基于学习和混沌的本地搜索来提高优化. 这种新的方法提高了对汇率的准确性和复杂工程问题的探索.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 生物启发的计算 生物启发的计算
背景情况:
- 在复杂的优化问题中,元启发式算法至关重要.
- 像Tunicate Swarm Algorithm (TSA) 这样的现有算法面临着诸如过早收和局部最佳陷等局限性.
- 用进化技术增强元启发学对于应对现代工程挑战至关重要.
研究的目的:
- 为了提高Tunicate Swarm算法 (TSA) 的效率和性能.
- 引入一种新的增强型变体,即准对立混沌TSA (QOCTSA),以克服TSA的局限性.
- 在优化中有效平衡勘探和开发能力.
主要方法:
- 通过将准对立式学习 (QOBL) 和混乱局部搜索 (CLS) 集成到TSA中,开发了准对立式混乱 TSA (QOCTSA).
- QOBL 提高了趋同准确度和勘探速度.
- 通过使用十个混乱地图,CLS通过改进本地搜索来提高利用率.
主要成果:
- 与原来的TSA相比,QOCTSA表现出更高的性能.
- 改进的算法在各种测试函数中表现出更快的融合率.
- 统计测试证实了QOCTSA在现实世界工程问题上比其他竞争算法优势.
结论:
- QOCTSA有效地解决了TSA的局限性,特别是过早的融合和局部优化.
- 整合QOBL和CLS显著提高了优化准确度,并保持了多元化.
- 在工程应用中,QOCTSA代表了解决复杂优化任务的有希望的进步.
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