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Volume 10
Issue 5
10.3390/biomimetics10050338
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning

by
Yu Liu
,
Maosheng Fu
,
Zhengyu Liu
*,
Huaiqing Liu
,
Wei Peng
,
Ling Li
,
Yang Yang
,
Xiancun Zhou
and
Chaochuan Jia
School of Electronics and Information Engineering, West Anhui University, Lu’an 237012, China
*
Author to whom correspondence should be addressed.
Biomimetics 2025, 10(5), 338; https://doi.org/10.3390/biomimetics10050338
Submission received: 8 April 2025 / Revised: 15 May 2025 / Accepted: 17 May 2025 / Published: 20 May 2025
Versions Notes

Abstract

The starfish optimization algorithm (SFOA) is a metaheuristic evolutionary intelligence algorithm with a great global search capability and strong adaptability. Although the SFOA has a good global search capability, it is not accurate enough in local search and converges slowly. To further enhance this convergence ability and global optimization ability, an enhanced starfish optimization algorithm (SFOAL) is proposed that combines sine chaotic mapping, t-distribution mutation, and logarithmic spiral reverse learning. The SFOAL can remarkably enhance both the global and local convergence capabilities of the algorithm, leading to a more rapid convergence speed and greater stability. In total, 23 benchmark functions and CEC2021 were used to test the development, search, and convergence capabilities of the SFOAL. The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. For solving practical problems, the SFOAL was used to optimize the back propagation (BP) neural network to solve the ultra-wideband line-of-sight positioning problem. The results showed that the SFOAL-BP neural network had a smaller average position error compared to the random BP neural network and the SFOA-BP neural network, so it can be used to solve practical application problems.
Keywords: sine chaotic mapping; t-distribution; logarithmic spiral reverse; metaheuristic sine chaotic mapping; t-distribution; logarithmic spiral reverse; metaheuristic

Share and Cite

MDPI and ACS Style

Liu, Y.; Fu, M.; Liu, Z.; Liu, H.; Peng, W.; Li, L.; Yang, Y.; Zhou, X.; Jia, C. An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning. Biomimetics 2025, 10, 338. https://doi.org/10.3390/biomimetics10050338

AMA Style

Liu Y, Fu M, Liu Z, Liu H, Peng W, Li L, Yang Y, Zhou X, Jia C. An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning. Biomimetics. 2025; 10(5):338. https://doi.org/10.3390/biomimetics10050338

Chicago/Turabian Style

Liu, Yu, Maosheng Fu, Zhengyu Liu, Huaiqing Liu, Wei Peng, Ling Li, Yang Yang, Xiancun Zhou, and Chaochuan Jia. 2025. "An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning" Biomimetics 10, no. 5: 338. https://doi.org/10.3390/biomimetics10050338

APA Style

Liu, Y., Fu, M., Liu, Z., Liu, H., Peng, W., Li, L., Yang, Y., Zhou, X., & Jia, C. (2025). An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning. Biomimetics, 10(5), 338. https://doi.org/10.3390/biomimetics10050338

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