MSHHOTSA: 一种突尼卡特群算法的变体,结合了多策略机制和混合哈里斯优化
Guangwei Liu1, Zhiqing Guo1, Wei Liu2
1College of Mining, Liaoning Technical University, Fuxin, Liaoning, China.
PloS one
|August 11, 2023
概括
一个新的多策略混合哈里斯·霍克斯 (Harris Hawks Tunicate) 团群优化算法 (MSHHOTSA) 提高了优化速度和准确性. 这种新的混合方法改善了对复杂问题的融合和探索.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
背景情况:
- 衣帽式集群算法 (TSA) 的优化速度缓慢,准确度低,在复杂问题上过早地趋同.
- 现有的元启发算法往往难以有效地平衡本地利用和全球探索.
- 需要强大的算法,能够处理多样化和具有挑战性的优化任务.
研究的目的:
- 为了引入多策略混合的哈里斯·霍克斯 (Harris Hawks Tunicate Swarm Optimization Algorithm,简称MSHHOTSA) 集群优化算法.
- 解决Tunicate Swarm算法 (TSA) 的局限性,包括缓慢的融合和过早的融合.
- 在速度,准确性和稳定性方面提高元启发算法的性能.
主要方法:
- 哈里斯·霍克斯优化 (HHO) 和Tunicate Swarm算法 (TSA) 机制的混合化.
- 引入一个过度的触角域来修改单独的位置,以改善收.
- 开发一个非线性收因子,以平衡本地开发和全球勘探.
- 将HHO群更新机制集成到TSA位置更新战略中.
主要成果:
- MSHHOTSA在标准基准函数,CEC2019基准函数,工程设计问题和PID参数优化方面展示了卓越的性能.
- 与其他七种元启发算法相比,拟议的算法表现出明显更高的局部融合和全球探索能力.
- 统计分析证实了MSHHOTSA增强的稳定性和通用性.
结论:
- MSHHOTSA有效地克服了原来的TSA的局限性,提供了更好的优化速度和准确性.
- 混合战略在本地开发和全球勘探之间提供了更好的平衡,从而带来了更强大的解决方案.
- MSHHOTSA代表了复杂计算问题的元启发式优化中的重大进步.
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