Chaotic manta ray foraging optimization based on elite hybridization and reverse learning

JingLing Zhang; Shaowei Gu; Tianle Liu; Tianlei Wang; Wenzhi Wu; Xizhu Chen

doi:10.1117/12.2644510

10 November 2022 Chaotic manta ray foraging optimization based on elite hybridization and reverse learning

JingLing Zhang, Shaowei Gu, Tianle Liu, Tianlei Wang, Wenzhi Wu, Xizhu Chen

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123011S (2022) https://doi.org/10.1117/12.2644510
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

Aiming at the shortcomings of manta ray foraging optimization, such as facile to sink into local optimal and sluggish convergence velocity, an improved manta ray foraging optimization for high-dimensional conundrums was proposed, which is named Chaotic Manta Ray Foraging Optimization based on Elite Hybridization and Reverse Learning (ERMRFO). Firstly, the initial population is initialized by Circle mapping, which makes the initial population traverse the space more randomly. Secondly, a multi-elite hybridization strategy was used to improve the poor particles. Finally, an adaptive reverse learning strategy combining Lévy flight and Gaussian variation was used to increase population diversity. Five high-dimensional test functions are selected for experiments, and the results show that the improved algorithm has better ability of escaping local optimum and faster convergence speed.

Citation Download Citation

JingLing Zhang, Shaowei Gu, Tianle Liu, Tianlei Wang, Wenzhi Wu, and Xizhu Chen "Chaotic manta ray foraging optimization based on elite hybridization and reverse learning", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123011S (10 November 2022); https://doi.org/10.1117/12.2644510

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