CLC number: TP301
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-09-13
Cited: 0
Clicked: 3565
Citations: Bibtex RefMan EndNote GB/T7714
Kai MENG, Chen CHEN, Bin XIN. MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200237 @article{title="MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization", %0 Journal Article TY - JOUR
MSSSA:一种针对全局优化问题的多策略增强型麻雀搜索算法1北京理工大学自动化学院,中国北京市,100081 2复杂系统智能控制与决策国家重点实验室,中国北京市,100081 摘要:麻雀搜索算法(SSA)是一种新的元启发式优化方法,具有简单和灵活的优点。然而,在处理多模态优化问题时,该算法仍存在早熟收敛、探索与开发不平衡等缺陷。针对上述问题,本文提出一种多策略增强的麻雀搜索算法(MSSSA)。首先,引入混沌映射以获取高质量的初始种群,并采用对立学习策略增加种群的多样性。其次,设计了一种自适应参数控制策略,以在全局探索与局部开发之间保持适当的平衡。最后,在个体更新阶段嵌入混合扰动机制,以避免算法陷入局部最优。为了验证所提方法的有效性,在IEEE CEC2014和IEEE CEC2019测试集的40个函数,以及10个不同维度的经典函数上进行了大量的实验。实验结果表明,与一些先进的算法相比,所提出的MSSSA表现出突出的优化性能。该算法还成功地应用于两个工程优化问题,证明了MSSSA在解决实际问题方面的优越性。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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