大规模优化问题的改进正弦余弦算法

Q4 Engineering

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2022-11-01 DOI:10.3724/sp.j.1249.2022.06684

Chao Zhang, Yezhou Yang

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引用次数: 1

摘要

体局部开发和逃离局部极值的能力;采用基于空间距离的非线性参数调整方法,平衡算法的局部开发和全局搜索,提高了算法的收敛速度.在14个经典测试函数上,维度分别为100、1 000和 5 000维时,与SCA、花授粉算法(flower pollination algorithm,FPA)、粒子群优化(particle swarm optimization,PSO)算法、麻雀搜索算法(sparrow search algorithm,SSA)和鲸鱼优化算法(whale optimization algorithm,WOA)5种群体智能算法进行仿真对比实验.结果表明,SCAL算法在收敛精度、收敛速度和鲁棒性上较 5种群体智能算法优势明显.与解决大规模优化问题的改进狼群算法(improved wolf pack algorithm,IWPA)、改进花授粉算法 (improved flower pollination algorithm,IFPA)、鲸鱼算法的两种改进版本 IWOA(improved whale optimization algorithm)和MWOA(modified whale optimization algorithm)进行比较,发现SCAL的整体寻优结果优于对比算

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Improved sine cosine algorithm for large-scale optimization problems

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来源期刊

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Engineering-Engineering (miscellaneous)

CiteScore

0.90

自引率

0.00%

发文量