Discrete fractional-order Caputo method to overcome trapping in local optima: Manta Ray Foraging Optimizer as a case study
作者：
Highlights：
• Novel memory-based fractional-order Caputo Manta ray foraging optimizer is proposed.
• Adaptively tuned somersault factor is used for balancing memory window and solutions.
• The proposed optimizer validated with CEC2017 and CEC2020 with several dimensions.
• The FCMRFO applicability is confirmed with engineering applications and segmentation.
• Comparison of the state-of-the-art techniques using non-parametric tests is performed.
摘要
•Novel memory-based fractional-order Caputo Manta ray foraging optimizer is proposed.•Adaptively tuned somersault factor is used for balancing memory window and solutions.•The proposed optimizer validated with CEC2017 and CEC2020 with several dimensions.•The FCMRFO applicability is confirmed with engineering applications and segmentation.•Comparison of the state-of-the-art techniques using non-parametric tests is performed.
论文关键词：Fractional-order,Caputo definition,Meta-heuristics (MH),Manta Ray Foraging Optimizer (MRFO),Engineering applications,Image segmentation
论文评审过程：Received 30 September 2020, Revised 28 November 2021, Accepted 28 November 2021, Available online 23 December 2021, Version of Record 31 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116355
