Multi-strategy Sea Horse Optimization Algorithm for UAV Path Planning

A modified Sea Horse Optimization algorithm (moSHO) designed for threat-aware unmanned aerial vehicle path planning. The dataset likely contains results from evaluating moSHO on 23 benchmark functions and UAV path planning experiments. Amir Seyyedabbasi authored this dataset, which was last updated on May 11, 2026.

Use Cases

• Benchmarking new optimization algorithms based on the 23 test functions described.
• Developing threat-aware path planning systems for UAVs based on the described cooperative strategies.
• Studying the balance between exploration and exploitation in metaheuristic algorithms based on the FAD, BWPM, and QRBL mechanisms.

Strengths

• Algorithm evaluated on 23 benchmark functions, suggesting a broad test scope.
• The description details three specific cooperative strategies (FAD, BWPM, QRBL) used to enhance the base algorithm.
• Dataset is small (4.1 KB), facilitating quick download and inspection.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset's 4.1 KB size indicates a very limited scope, likely containing summary results rather than raw simulation data.

Provenance

Source

figshare

Collection Method

Likely contains experimental results from algorithm simulations.

Freshness

Last updated 2026-05-11 14:08:36; freshness should be verified.