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Related Experiment Video

Updated: Jan 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimizing Maritime Search and Rescue Planning via Genetic Algorithms: Incorporating Civilian Vessel Collaboration.

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  • 1Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea.

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|September 26, 2025
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Summary
This summary is machine-generated.

This study uses a Genetic Algorithm (GA) for maritime Search and Rescue (SAR) planning, maximizing target detection by optimizing Search and Rescue Unit (SRU) deployment. The GA approach proves more effective and stable than baseline methods, especially with civilian SRUs.

Keywords:
civilian cooperationgenetic algorithmgreedy algorithmsearch and rescue planning

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Maritime Safety

Background:

  • Maritime Search and Rescue (SAR) operations face challenges in efficiently deploying Search and Rescue Units (SRUs) to maximize target detection.
  • Existing methods may lack flexibility and scalability for real-time, dynamic SAR planning.
  • Integrating civilian SRUs offers potential for enhanced coverage but requires optimized deployment strategies.

Purpose of the Study:

  • To develop and evaluate a biomimetic optimization approach for maritime SAR planning using a Genetic Algorithm (GA).
  • To maximize the detection of drifting targets by optimally deploying both official and civilian SRUs.
  • To assess the GA's performance against a baseline evolutionary algorithm with greedy deployment.

Main Methods:

  • A Genetic Algorithm (GA) was employed for optimizing SRU deployment in maritime SAR.
  • The GA incorporated a Probability of Detection (POD)-adjusted fitness function with collision-avoidance constraints.
  • A greedy initialization strategy was integrated to enhance the GA's performance.
  • The GA was compared against an Evolutionary Algorithm with Greedy Deployment (EAGD) across 24 experimental conditions.

Main Results:

  • The GA consistently achieved higher average fitness and demonstrated superior stability compared to the EAGD baseline.
  • The GA showed particular effectiveness in stress-test scenarios involving only civilian SRUs.
  • The biomimetic approach proved robust across various maritime scenarios and coverage conditions.

Conclusions:

  • Biomimetic algorithms, specifically the GA, offer a promising approach for real-time, flexible, and scalable maritime SAR planning.
  • The study highlights the significant value of incorporating civilian SRUs into emergency maritime operations through optimized deployment.
  • The proposed GA method provides an effective strategy for enhancing SAR mission efficiency and success rates.