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Related Experiment Video

Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Multi-strategy Sea Horse Optimization algorithm for UAV path planning.

Amir Seyyedabbasi1, Bahman Arasteh2,3, Ahmet Gurhanli4

  • 1Computer Engineering Department, Istinye University, Istanbul, Türkiye.

Frontiers in Robotics and AI
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

A new modified Sea Horse Optimization (moSHO) algorithm enhances unmanned aerial vehicle (UAV) path planning by improving exploration and exploitation. This method reliably finds safe paths in threat environments.

Keywords:
Sea Horse Optimizationmetaheuristicsmulti-strategyopposite-based learningunmanned aerial vehicle path planning

Related Experiment Videos

Last Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Unmanned aerial vehicle (UAV) path planning is crucial for autonomous navigation but presents complex optimization challenges.
  • Traditional and metaheuristic methods often struggle with constraints and premature convergence to local optima.

Purpose of the Study:

  • To introduce a modified Sea Horse Optimization (moSHO) algorithm for effective threat-aware UAV path planning.
  • To enhance the exploration and exploitation balance in optimization for complex navigation tasks.

Main Methods:

  • Developed moSHO by integrating three cooperative strategies into the Sea Horse Optimization algorithm: fish-aggregating device (FAD) mechanism, best-worst position mutation (BWPM), and quasi-reflection-based learning (QRBL).
  • FAD promotes diversity via adaptive perturbations.
  • BWPM refines elite solutions and guides weaker ones.
  • QRBL enhances exploration using quasi-opposite candidates.

Main Results:

  • The moSHO algorithm demonstrated robustness across 23 benchmark functions.
  • Experiments confirmed moSHO's reliability in identifying safe and feasible UAV paths within threat environments.
  • The integrated strategies improved exploration without sacrificing exploitation, leading to a balanced optimization process.

Conclusions:

  • The proposed moSHO algorithm offers a superior approach to UAV path planning in complex, threat-aware scenarios.
  • moSHO effectively overcomes limitations of traditional methods and enhances autonomous navigation capabilities.