Correct algorithm name

Author: Daniel Precioso

_posts/2025-01-01-hadad.md

@@ -18,7 +18,7 @@ We are thrilled to kick off 2025 by announcing our newest scientific contributio
 
 Weather Navigation focuses on developing cutting-edge optimization methods to address the challenges of weather routing. In this publication, we introduce HADAD (Hexagonal A-Star with Differential Algorithm Designed for weather routing), a novel approach that first applies an A* search on a hexagonal grid with extended neighbor relationships, allowing more directional flexibility than standard graph-based searches. This global exploration is then refined by a discrete Newton–Jacobi variational technique, guaranteeing convergence to a locally optimal, smoothly curved path in continuous space.
 
-<img src="{{ page.featured_image }}" alt="Hybrid Search" width="100%"/>
+<img src="{{ page.featured_image }}" alt="HADAD" width="100%"/>
 
 To assess HADAD's performance, we compiled a comprehensive benchmark of 1,560 test scenarios covering an entire year, featuring diverse origin-destination pairs, vessel speeds, and environmental conditions. Our findings indicate that HADAD provides an additional 4% improvement over pure A* graph methods compared to the shortest-distance route, thanks to its flexible, gradient-based refinements. A seasonal analysis reveals markedly higher gains in winter, sometimes doubling those in summer, with savings peaking at 27% under extreme weather. Additionally, testing the algorithm with synthetic vector fields demonstrated its adaptability for fuel consumption optimization and Just-in-Time arrivals.