multi-objective logging

Hi, 

I'm trying to solve a hierarchical multi-objective ILP with gurobi 10.0. 

When the model is solved, I retrieve the objective values with:

nSolutions  = model.SolCount
nObjectives = model.NumObj    

for s in range(nSolutions):
    model.params.SolutionNumber = s
    print('Solution', s, ':', end='\n')
    for o in range(nObjectives):
        model.params.ObjNumber = o
        print(' ',model.ObjNVal, end='\n')

This gives me the following solution: 

Solution 0 :
  1074757567.0
  44910059.0
Solution 1 :
  1122697741.0
  46681612.0
Solution 2 :
  1126893891.0
  47306774.0

As I also solved the single-objective problem before, I know that the returned objective value of objective 1 of the first solution is right.

When I look at the gurobi log, however, the incumbent seems to be even better and none of the solutions in "solution count" is returned by model.ObjNVal. 

Optimize a model with 8236 rows, 17755 columns and 68217 nonzeros
Variable types: 0 continuous, 17755 integer (17749 binary)
Coefficient statistics:
  Matrix range     [8e-03, 3e+04]
  Objective range  [1e+03, 7e+07]
  Bounds range     [1e+00, 1e+00]
  RHS range        [1e+00, 2e+02]

---------------------------------------------------------------------------
Multi-objectives: starting optimization with 2 objectives ... 
---------------------------------------------------------------------------

Multi-objectives: applying initial presolve ...
---------------------------------------------------------------------------

Presolve removed 8229 rows and 12014 columns
Presolve time: 0.02s
Presolved: 7 rows and 5741 columns
---------------------------------------------------------------------------

Multi-objectives: optimize objective 1 (LCC) ...
---------------------------------------------------------------------------

Found heuristic solution: objective 1.083384e+09
Presolve removed 0 rows and 3772 columns
Presolve time: 0.00s
Presolved: 7 rows, 1969 columns, 4073 nonzeros
Found heuristic solution: objective 1.079187e+09
Variable types: 0 continuous, 1969 integer (1897 binary)

Root relaxation: objective 1.024916e+09, 5 iterations, 0.00 seconds (0.00 work units)

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1.0249e+09    0    2 1.0792e+09 1.0249e+09  5.03%     -    0s
     0     0 1.0309e+09    0    1 1.0792e+09 1.0309e+09  4.48%     -    0s
H    0     0                    1.031247e+09 1.0309e+09  0.03%     -    0s
     0     0 1.0309e+09    0    1 1.0312e+09 1.0309e+09  0.03%     -    0s

Explored 1 nodes (9 simplex iterations) in 0.08 seconds (0.05 work units)
Thread count was 8 (of 8 available processors)

Solution count 3: 1.03125e+09 1.07919e+09 1.08338e+09 

Optimal solution found (tolerance 0.00e+00)
Best objective 1.031247217000e+09, best bound 1.031247217000e+09, gap 0.0000%
---------------------------------------------------------------------------

Multi-objectives: optimize objective 2 (CO2-eq) ...
---------------------------------------------------------------------------


Loaded user MIP start with objective 1.52736e+07

Presolve removed 0 rows and 3781 columns
Presolve time: 0.03s
Presolved: 8 rows, 1960 columns, 6005 nonzeros
Variable types: 0 continuous, 1960 integer (1890 binary)

Root relaxation: objective 1.527358e+07, 28 iterations, 0.00 seconds (0.00 work units)

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1.5274e+07    0    2 1.5274e+07 1.5274e+07  0.00%     -    0s

Explored 1 nodes (28 simplex iterations) in 0.11 seconds (0.07 work units)
Thread count was 8 (of 8 available processors)

Solution count 1: 1.52736e+07 

Optimal solution found (tolerance 1.00e-04)
Best objective 1.527357700000e+07, best bound 1.527357611111e+07, gap 0.0000%

---------------------------------------------------------------------------
Multi-objectives: solved in 0.13 seconds (0.07 work units), solution count 3

Do you have any idea/hint, why model.ObjNVal and logging values differ?

Thanks for your help!