Consider negative and positive zero in objective layer

rosomaxa/src/algorithms/gsom/network.rs

@@ -183,7 +183,7 @@ where
 
     /// Returns max unified distance of the network.
     pub fn max_unified_distance(&self) -> Float {
-        self.get_nodes().map(|node| node.unified_distance(self, 1)).max_by(|a, b| a.total_cmp(b)).unwrap_or(0.)
+        self.get_nodes().map(|node| node.unified_distance(self, 1)).max_by(|a, b| a.total_cmp(b)).unwrap_or_default()
     }
 
     /// Trains network on an input.

rosomaxa/src/termination/mod.rs

@@ -66,6 +66,6 @@ where
     }
 
     fn estimate(&self, heuristic_ctx: &Self::Context) -> Float {
-        self.terminations.iter().map(|t| t.estimate(heuristic_ctx)).max_by(|a, b| a.total_cmp(b)).unwrap_or(0.)
+        self.terminations.iter().map(|t| t.estimate(heuristic_ctx)).max_by(|a, b| a.total_cmp(b)).unwrap_or_default()
     }
 }

vrp-core/src/models/goal.rs

@@ -205,7 +205,13 @@ impl GoalBuilder {
                 let fitness_a = objectives[0].fitness(a);
                 let fitness_b = objectives[0].fitness(b);
 
-                fitness_a.total_cmp(&fitness_b)
+                // NOTE total_cmp distinguishes between positive zero and negative zero while
+                // logically they are the same in this context
+                if fitness_a == 0. && fitness_b == 0. {
+                    Ordering::Equal
+                } else {
+                    fitness_a.total_cmp(&fitness_b)
+                }
             }),
             Arc::new(|objectives, move_ctx| objectives[0].estimate(move_ctx)),
             vec![objective],