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.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
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.
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.
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