Beyond a Single Plan: Genetic Algorithms for Flexible Maritime Search and Rescue

Abstract

In maritime search and rescue (SAR) operations, the estimated location of survivors spreads over time, causing the search area to expand continuously. Deploying limited search and rescue units (SRUs) efficiently is critical, but as the search area grows, the number of possible deployment combinations increases exponentially, making exhaustive search impractical. This study proposes a two-stage metaheuristic-based optimization framework that balances computational efficiency and solution quality. In the first stage, the search area is discretized into a grid using simulated particle diffusion results, and grid cell importance is estimated based on particle distribution to assign limited SRUs to high-priority cells first. In the second stage, SRU deployment within each selected cell is reformulated as a permutation-based matching problem to refine resource allocation. The proposed framework was evaluated across 72 scenarios using genetic algorithm, simulated annealing, particle swarm optimization, and differential evolution. Results show that genetic algorithm consistently outperformed other algorithms under resource-constrained conditions while maintaining high solution diversity, providing multiple high-quality alternatives for practical SAR decision-making.