An improved multi-strategy equilibrium optimizer for surface marine vehicle path planning
Abstract To address the limitations of the standard equilibrium optimizer (EO) in terms of insufficient optimization capability, multiple strategies are proposed to enhance its performance. These include a reverse equilibrium state pool, a non-uniform equilibrium state selection strategy, and an equ...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-15316-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract To address the limitations of the standard equilibrium optimizer (EO) in terms of insufficient optimization capability, multiple strategies are proposed to enhance its performance. These include a reverse equilibrium state pool, a non-uniform equilibrium state selection strategy, and an equilibrium state mutation strategy. The reverse equilibrium state pool is introduced to encourage candidate solutions with poorer positions to search in a wider search space, under such considerations the global search ability of the improved EO can be enhanced. The non-uniform equilibrium state selection strategy is proposed to select equilibrium state. Under the proposed selection strategy, the candidate solutions with better positions are more likely to be chosen as the equilibrium state, allowing for sufficient exploration of positions near the current optimal point. The equilibrium state mutation strategy leads to cross mutation between candidate solutions and equilibrium state, increasing the likelihood of the group exploring the global optimal solution. To verify and further analyze the performance and superiority of the improved EO, i.e., reverse equilibrium states EO (R $$\mathrm {E^{2}}$$ O), 29 benchmark functions are adopted. It is verified theoretically from the experimental results that the R $$\mathrm {E^{2}}$$ O is with a significant improvement in performance by comparison between the standard EO and certain frequently-used heuristic optimization algorithms. Finally, the R $$\mathrm {E^{2}}$$ O is successfully applied in path planning for surface marine vehicles under the situations of both dynamic and static obstacles. |
|---|---|
| ISSN: | 2045-2322 |