Update

Author: odow

src/algorithms/Chalmet.jl

@@ -57,7 +57,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, solutions
+        return status, nothing
     end
     _, y1[2] = _compute_point(model, variables, f2)
     MOI.set(model.inner, MOI.ObjectiveFunction{typeof(f1)}(), f1)
@@ -69,7 +69,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, solutions
+        return status, nothing
     end
     x1, y1[1] = _compute_point(model, variables, f1)
     MOI.delete(model.inner, y1_constraint)
@@ -78,7 +78,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, solutions
+        return status, nothing
     end
     _, y2[1] = _compute_point(model, variables, f1)
     if y2[1] ≈ solutions[1].y[1]
@@ -93,18 +93,16 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, solutions
+        return status, nothing
     end
     x2, y2[2] = _compute_point(model, variables, f2)
     MOI.delete(model.inner, y2_constraint)
     push!(solutions, SolutionPoint(x2, y2))
     push!(Q, (1, 2))
     t = 3
-    status = MOI.OPTIMAL
     while !isempty(Q)
         if (ret = _check_premature_termination(model, start_time)) !== nothing
-            status = ret
-            break
+            return ret, solutions
         end
         r, s = pop!(Q)
         yr, ys = solutions[r].y, solutions[s].y
@@ -118,5 +116,5 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
         append!(Q, [(r, t), (t, s)])
         t += 1
     end
-    return status, solutions
+    return MOI.OPTIMAL, solutions
 end

src/algorithms/Dichotomy.jl

@@ -112,7 +112,8 @@ function optimize_multiobjective!(algorithm::Dichotomy, model::Optimizer)
         w = y_d[2] / (y_d[2] - y_d[1])
         status, solution = _solve_weighted_sum(model, algorithm, [w, 1.0 - w])
         if !_is_scalar_status_optimal(status)
-            break # Exit the solve with some error.
+            # Exit the solve with some error.
+            return status, nothing
         elseif solution ≈ solutions[a] || solution ≈ solutions[b]
             # We have found an existing solution. We're free to prune (a, b)
             # from the search space.

src/algorithms/DominguezRios.jl

@@ -57,8 +57,8 @@ function _p_partition(
     ẑ = max.(z, B.l)
     ret = _DominguezRiosBox[]
     for i in 1:length(z)
-        new_l = vcat(B.l[1:i], ẑ[(i+1):end])
-        new_u = vcat(B.u[1:(i-1)], ẑ[i], B.u[(i+1):end])
+        new_l = vcat(B.l[1:i], ẑ[i+1:end])
+        new_u = vcat(B.u[1:i-1], ẑ[i], B.u[i+1:end])
         new_priority = _reduced_scaled_priority(new_l, new_u, i, ẑ, yI, yN)
         push!(ret, _DominguezRiosBox(new_l, new_u, new_priority))
     end