Dynamic sine cosine algorithm for large-scale global optimization problems
作者：
Highlights：
• Sine cosine algorithm is improved to solve large-scale global optimization problem.
• Random convergence parameter is added to balance the exploration and exploitation.
• Dynamic inertia weight is introduced to modify equation to accelerate convergence.
• 15 benchmark functions and CEC2010 functions are taken to evaluate performance.
• Superiority of modified algorithm is also confirmed by solving engineering problem.
摘要
•Sine cosine algorithm is improved to solve large-scale global optimization problem.•Random convergence parameter is added to balance the exploration and exploitation.•Dynamic inertia weight is introduced to modify equation to accelerate convergence.•15 benchmark functions and CEC2010 functions are taken to evaluate performance.•Superiority of modified algorithm is also confirmed by solving engineering problem.
论文关键词：Sine cosine algorithm,Large-scale global optimization,Random convergence parameter,Dynamic inertia weight,Engineering design problems
论文评审过程：Received 13 December 2019, Revised 8 May 2020, Accepted 24 March 2021, Available online 30 March 2021, Version of Record 12 April 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114950
