Ensure filter_nondominated is called for all algorithms (#168)

Author: odow

src/MultiObjectiveAlgorithms.jl

@@ -869,8 +869,8 @@ function _optimize!(model::Optimizer)
     status, solutions = optimize_multiobjective!(algorithm, model)
     model.termination_status = status
     if solutions !== nothing
-        model.solutions = solutions
-        _sort!(model.solutions, MOI.get(model, MOI.ObjectiveSense()))
+        sense = MOI.get(model, MOI.ObjectiveSense())
+        model.solutions = filter_nondominated(sense, solutions)
     end
     if !model.silent
         _print_footer(stdout, model)

src/algorithms/EpsilonConstraint.jl

@@ -133,5 +133,5 @@ function minimize_multiobjective!(
         bound = min(Y[1] - constant - ε, bound - ε)
     end
     MOI.delete(model, ci)
-    return status, filter_nondominated(MOI.MIN_SENSE, solutions)
+    return status, solutions
 end

src/algorithms/KirlikSayin.jl

@@ -194,5 +194,5 @@ function minimize_multiobjective!(algorithm::KirlikSayin, model::Optimizer)
         MOI.delete.(model, ε_constraints)
         MOI.delete(model, zₖ_constraint)
     end
-    return status, filter_nondominated(MOI.MIN_SENSE, solutions)
+    return status, solutions
 end

src/algorithms/Lexicographic.jl

@@ -110,8 +110,7 @@ function optimize_multiobjective!(algorithm::Lexicographic, model::Optimizer)
             break
         end
     end
-    sense = MOI.get(model.inner, MOI.ObjectiveSense())
-    return status, filter_nondominated(sense, solutions)
+    return status, solutions
 end
 
 function _solve_in_sequence(

src/algorithms/RandomWeighting.jl

@@ -81,5 +81,5 @@ function optimize_multiobjective!(algorithm::RandomWeighting, model::Optimizer)
         end
         solutions = filter_nondominated(sense, solutions)
     end
-    return MOI.OPTIMAL, filter_nondominated(sense, solutions)
+    return MOI.OPTIMAL, solutions
 end

test/algorithms/Sandwiching.jl

@@ -93,13 +93,12 @@ function test_molp_2()
     C = Float64[3 -2; -1 2]
     A = Float64[-4 -8; 3 -6; 4 -2; 1 0; -1 3; -2 4; -6 3]
     b = Float64[-8, 6, 14, 6, 15, 18, 9]
-    results = sort([
-        [1.0, 5.0] => [-7.0, 9.0], # not sure about this
+    results = [
         [3.0, 6.0] => [-3.0, 9.0],
         [4.0, 1.0] => [10.0, -2.0],
         [6.0, 5.0] => [8.0, 4.0],
         [6.0, 7.0] => [4.0, 8.0],
-    ])
+    ]
     sense = MOI.MAX_SENSE
     return _test_molp(C, A, b, results, sense)
 end