Version 1
: Received: 6 August 2024 / Approved: 7 August 2024 / Online: 8 August 2024 (12:10:13 CEST)
How to cite:
Zheng, K.; Liu, H.; Jia, T. Fusion of RIME Algorithm Improved Snake Optimization Algorithm and Its Applications in UAV Path Planning and Engineering Problems. Preprints2024, 2024080581. https://doi.org/10.20944/preprints202408.0581.v1
Zheng, K.; Liu, H.; Jia, T. Fusion of RIME Algorithm Improved Snake Optimization Algorithm and Its Applications in UAV Path Planning and Engineering Problems. Preprints 2024, 2024080581. https://doi.org/10.20944/preprints202408.0581.v1
Zheng, K.; Liu, H.; Jia, T. Fusion of RIME Algorithm Improved Snake Optimization Algorithm and Its Applications in UAV Path Planning and Engineering Problems. Preprints2024, 2024080581. https://doi.org/10.20944/preprints202408.0581.v1
APA Style
Zheng, K., Liu, H., & Jia, T. (2024). Fusion of RIME Algorithm Improved Snake Optimization Algorithm and Its Applications in UAV Path Planning and Engineering Problems. Preprints. https://doi.org/10.20944/preprints202408.0581.v1
Chicago/Turabian Style
Zheng, K., Huiyong Liu and Tongyu Jia. 2024 "Fusion of RIME Algorithm Improved Snake Optimization Algorithm and Its Applications in UAV Path Planning and Engineering Problems" Preprints. https://doi.org/10.20944/preprints202408.0581.v1
Abstract
In aerospace, robotics, network engineering and mechanical engineering, there exist complex and urgent challenges that can be abstracted into mathematical optimization problems. Therefore, exploring solutions to these optimization problems is a crucial task. Metaheuristic algorithms have been applied across various fields.The Snake Optimization Algorithm (SO) is an innovative metaheuristic method recognized for its efficient solving capabilities.However, this algorithm encounters challenges such as diminished search efficiency in the later stages and a propensity to become ensnared in local optima. To mitigate these issues, this paper introduces an enhanced Snake Optimization Algorithm (ISO). ISO integrates the RIME Algorithm(RIME)and introduces two effective enhancement strategies to improve its capability to evade local optima.To assess the effectiveness of ISO,the population distribution state was first measured using three algorithms, including Star Discrepancy, and the exploration and exploitation performance was analyzed using the CEC2017 test functions. Subsequently, utilized 23 benchmark functions for testing.The evaluation outcomes indicate that ISO exhibits outstanding performance in terms of convergence velocity and robustness.Additionally, it was applied to engineering fields including UAV path planning,Robot path planning, Wireless sensor network node deployment and Pressure vessel design.In comparison to the SO algorithm, the ISO algorithm demonstrates superior stability, converges earlier, and improves its ability to solve for optimal .These findings highlight the significant potential of the ISO algorithm in diverse test functions and a wide range of interdisciplinary problems.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
