Improved marine predators algorithm with refracted opposition-based learning

Wentao Chen; Beibei Song; Junchao Cheng; Jinxian Han

doi:10.1117/12.2639858

19 October 2022 Improved marine predators algorithm with refracted opposition-based learning

Wentao Chen, Beibei Song, Junchao Cheng, Jinxian Han

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944D (2022) https://doi.org/10.1117/12.2639858
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

This paper aims at the shortcomings of marine predators algorithm, such as low initial population quality and easy to fall into local optimum. In this paper, an improved marine predator algorithm is proposed to solve the above problems. Firstly, the algorithm uses opposition-based learning to generate high quality initial population. Secondly, the algorithm applies refracted opposition-based learning and opposition-based learning according to individual fitness. This method can generate high quality populations for local and global scope to improve population diversity obviously. Then, the adaptive mechanism is used to prevent the function from falling into local optimum at the later stage of iteration. Finally, the algorithm selects 14 test functions to verify performance.

Citation Download Citation

Wentao Chen, Beibei Song, Junchao Cheng, and Jinxian Han "Improved marine predators algorithm with refracted opposition-based learning", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944D (19 October 2022); https://doi.org/10.1117/12.2639858

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