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  • 1Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003 Hradec Kralove, Czech Republic.

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概括
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关键词:
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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 超启发式计算 超启发式计算

背景情况:

  • 优化问题在科学和工程中无处不在.
  • 现有的元启发算法经常在平衡探索和利用方面扎.
  • 粒子优化 (PSO) 在本地搜索 (利用) 中表现出色,而基于教学学习的优化 (TLBO) 在全球搜索 (探索) 中表现强.

研究的目的:

  • 通过整合PSO和TLBO,开发一种新的混合元启发算法hPSO-TLBO.
  • 通过将PSO的开采与TLBO的勘探相结合来增强搜索能力.
  • 评估hPSO-TLBO在基准函数和现实工程问题上的有效性.

主要方法:

  • 一个混合算法,hPSO-TLBO,通过将TLBO的教师阶段与PSO的速度方程相结合来设计.
  • 提升了TLBO的学习阶段,使学生能够从优秀的同龄人那里学习.
  • 该算法经过数学建模,并对单模,多模和高维基基准函数进行了严格测试,包括CEC 2017的问题.

主要成果:

  • hPSO-TLBO在各种基准功能中表现出色.
  • 该算法有效地平衡了搜索空间的探索和利用.
  • 对比分析显示,hPSO-TLBO的表现始终优于其他12个元启发算法.

结论:

  • 拟议的hPSO-TLBO算法为解决复杂的优化问题提供了一种强大而有效的方法.
  • 它的卓越性能凸显了它在解决具有挑战性的现实世界工程应用中的潜力.
  • 混合战略成功地利用了公用事业机关和TLBO的优势.