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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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一个多策略改进的北方戈斯霍克优化算法,用于优化工程问题.

Haijun Liu1, Jian Xiao1, Yuan Yao2

  • 1School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China.

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概括
此摘要是机器生成的。

多策略改进的北戈肖克优化 (MSINGO) 算法通过解决局部最佳和缓慢的融合来增强原来的非政府组织. MSINGO在各种测试函数和现实世界问题上的探索,利用和可扩展性方面表现出卓越的性能.

关键词:
立方地图策略立方地图策略北部戈斯霍克优化优化权重的正弦值和正弦值优化策略.权重随机差异突变策略的权重随机差异.

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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:

  • 北方戈斯霍克优化 (NGO) 算法是一种高效的元启发式,但受到局部最佳陷和缓慢融合的影响.
  • 解决这些局限性对于提高优化技术的实际应用性至关重要.

研究的目的:

  • 为了引入多策略改进的北方鱼优化 (MSINGO) 算法.
  • 为了提高原始NGO算法的探索,利用和融合速度.

主要方法:

  • MSINGO算法将立方映射,加权随机差异突变和加权正弦值和正弦值优化策略集成到NGO框架中.
  • 性能评估涉及使用CEC2017测试函数对五个高度引用和六个最近的元启发算法进行比较实验.
  • 该算法的有效性在六个真实世界的工程问题上得到了进一步的验证.

主要成果:

  • MSINGO在利用能力,勘探能力,局部最佳规避和CEC2017测试函数的可扩展性方面明显优于竞争对手的算法.
  • 实验结果表明,与原来的NGO和其他基准算法相比,该算法得到了显著改进.
  • 该算法在应用于现实世界的工程挑战时展示了实际的实用性和潜力.

结论:

  • 拟议的MSINGO算法有效地克服了原来的非政府组织的缺点,特别是避免了局部最佳并加速了融合.
  • 与现有的元启发算法相比,MSINGO表现出强大的性能和卓越的能力.
  • 该研究强调了MSINGO在解决各种领域复杂的优化问题的潜力.