A Risk-Driven Maritime Patrol Route Optimization Framework for IUU Fishing Surveillance Using Multi-Source AIS and SAR Data Fusion

Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Conventional patrol strategies under-utilize the available multi-source surveillance data. This study proposes a maritime patrol-routing framework that integrates AIS fishing effort, Sentinel-1 SAR dark-vessel detections, and GFW vessel encounter records into a Surveillance Priority Index (SPI) over the study domain (0–20°N, 140–160°E). An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware route construction and best-of-N adaptive strategy selection produces patrol routes that cover high-priority cells within a 72 h window while minimizing total distance. Across 30 random seeds and a benchmark suite (PB-ACO, GA, PSO, DQN, NSGA-II), APB-ACO yields the shortest mean route ($21,658\pm 9$ km, $7\%$ shorter than PB-ACO, $p<0.001$), the lowest variance ($46\times$ lower standard deviation than PB-ACO), and 100% high-priority coverage at default settings; a scalability analysis across 2–20% high-priority task ratios shows that the coverage gap over PB-ACO widens with the HP ratio. The problem is also formalized as a Mixed-Integer Linear Program (Priority-Constrained VRPTW), positioning APB-ACO as a constructive metaheuristic for an NP-hard operational problem. The framework’s principal limitation is that, in the tested three-vessel scenario, the 500 km inter-vessel communication constraint is violated more than 1,100 times per 72 h mission and is repaired post hoc; integrating this constraint into the optimizer is identified as a near-term extension. The results provide a methodological foundation for surveillance-driven patrol planning rather than a validated tool for operational IUU interdiction.