A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization DOI Creative Commons
G. Gu, J. L. Lou,

Haibo Wan

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

Abstract The RIME optimization algorithm (RIME) represents an advanced technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response these shortcomings, we propose a multi-strategy enhanced version known the improved (MIRIME). Firstly, Tent chaotic map is utilized initialize population, laying groundwork for global optimization. Secondly, introduce adaptive update strategy based on leadership dynamic centroid, facilitating swarm's exploitation in more favorable direction. To address problem of population scarcity later iterations, lens imaging opposition-based learning control introduced enhance diversity ensure accuracy. proposed centroid boundary not only limits search boundaries individuals but also effectively enhances algorithm's focus efficiency. Finally, demonstrate performance MIRIME, employ 30 CEC2017 test functions compare with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, assess method's practical feasibility, apply MIRIME solve three-dimensional path planning unmanned surface vehicles. Experimental results indicate that outperforms other competing terms solution quality stability, highlighting superior application potential.

Language: Английский

A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization DOI Creative Commons
G. Gu, J. L. Lou,

Haibo Wan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 1, 2024

The RIME optimization algorithm (RIME) represents an advanced technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response these shortcomings, we propose a multi-strategy enhanced version known the improved (MIRIME). Firstly, Tent chaotic map is utilized initialize population, laying groundwork for global optimization. Secondly, introduce adaptive update strategy based on leadership dynamic centroid, facilitating swarm's exploitation in more favorable direction. To address problem of population scarcity later iterations, lens imaging opposition-based learning control introduced enhance diversity ensure accuracy. proposed centroid boundary not only limits search boundaries individuals but also effectively enhances algorithm's focus efficiency. Finally, demonstrate performance MIRIME, employ CEC 2017 2022 test suites compare with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, assess method's practical feasibility, apply MIRIME solve three-dimensional path planning unmanned surface vehicles. Experimental results indicate that outperforms other competing terms solution quality stability, highlighting superior application potential.

Language: Английский

Citations

2
A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization DOI Creative Commons
G. Gu, J. L. Lou,

Haibo Wan

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

Abstract The RIME optimization algorithm (RIME) represents an advanced technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response these shortcomings, we propose a multi-strategy enhanced version known the improved (MIRIME). Firstly, Tent chaotic map is utilized initialize population, laying groundwork for global optimization. Secondly, introduce adaptive update strategy based on leadership dynamic centroid, facilitating swarm's exploitation in more favorable direction. To address problem of population scarcity later iterations, lens imaging opposition-based learning control introduced enhance diversity ensure accuracy. proposed centroid boundary not only limits search boundaries individuals but also effectively enhances algorithm's focus efficiency. Finally, demonstrate performance MIRIME, employ 30 CEC2017 test functions compare with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, assess method's practical feasibility, apply MIRIME solve three-dimensional path planning unmanned surface vehicles. Experimental results indicate that outperforms other competing terms solution quality stability, highlighting superior application potential.

Language: Английский

Citations

0