Multi-strategy Sea Horse Optimization Algorithm for UAV Path Planning

A 5.7 KB CSV file presents a modified Sea Horse Optimization algorithm (moSHO) for threat-aware unmanned aerial vehicle path planning. The dataset, authored by Amir Seyyedabbasi and last updated in May 2026, describes an algorithm that integrates three cooperative strategies to improve exploration and avoid local optima. It includes evaluation results from 23 benchmark functions and experiments on a UAV path planning model under threat environments.

Use Cases

• Benchmarking new metaheuristic algorithms based on the 23 benchmark functions mentioned.
• Developing threat-aware UAV navigation systems based on the described path planning model.
• Studying hybrid optimization strategies that combine FAD mechanisms, Gaussian adjustments, and quasi-opposition learning.
• Comparing algorithm performance for constrained optimization problems in robotics.

Strengths

• The algorithm is evaluated on 23 benchmark functions, providing a standard test suite.
• The description details three specific cooperative strategies (FAD, BWPM, QRBL) for algorithmic improvement.
• File size is small (5.7 KB), ensuring quick download and inspection.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.7 KB), indicating limited scope, likely containing algorithm parameters or results rather than extensive simulation data.

Provenance

Source

Amir Seyyedabbasi via figshare

Collection Method

Likely contains algorithmic parameters, benchmark results, or path planning model outputs.

Freshness

Last updated 2026-05-11 14:08:35