Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies
作者：
Highlights：
• Orthogonal learning, multi-population, and greedy selection are embedded into sine cosine algorithm.
• Orthogonal learning procedure is introduced to improve its neighborhood search capabilities.
• Multi-population scheme with three sub-strategies is adopted to enhance the global exploration capabilities.
• Greedy selection strategy is applied to improve the qualities of the search agents.
• Extensive benchmark problems and methods are used to verify the proposed method.
摘要
•Orthogonal learning, multi-population, and greedy selection are embedded into sine cosine algorithm.•Orthogonal learning procedure is introduced to improve its neighborhood search capabilities.•Multi-population scheme with three sub-strategies is adopted to enhance the global exploration capabilities.•Greedy selection strategy is applied to improve the qualities of the search agents.•Extensive benchmark problems and methods are used to verify the proposed method.
论文关键词：Sine cosine algorithm,Orthogonal learning,Multi-swarm,Greedy selection
论文评审过程：Received 5 May 2019, Revised 8 November 2019, Accepted 29 November 2019, Available online 30 November 2019, Version of Record 11 December 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113113
