Performance of algorithms

Xavier says

> I recently tried to solve an instance with TambyVanderpooten, [MOKP_p-6_n-30_1.dat](https://github.com/tambysatya/TamVan19/blob/master/Instances/MOKP/MOKP_p-6_n-30_1.dat), which is on Tamby's repo and presented as solved by their algorithm in the paper. It does not run on the MOA implementation (nothing after 24 hours)

Here's my Julia code:
```Julia
using JuMP, HiGHS
import MultiObjectiveAlgorithms as MOA

function solve_tamby_mokp(filename)
lines = readlines(filename)
p = parse(Int, lines[1])
n = parse(Int, lines[2])
b = parse(Float64, lines[3])
C = collect(reduce(hcat, Float64.(x.args) for x in Meta.parse(lines[4]).args)')
w = Meta.parse(lines[5]).args
model = Model(() -> MOA.Optimizer(HiGHS.Optimizer))
set_attribute(model, MOA.Algorithm(), MOA.TambyVanderpooten())
@variable(model, x[1:n], Bin)
@constraint(model, w' * x <= b)
@objective(model, Max, C * x)
optimize!(model)
assert_is_solved_and_feasible(model)
return [value.(x; result) for result in 1:result_count(model)]
end

solve_tamby_mokp(
"/Users/odow/git/tambysatya/TamVan19/Instances/MOKP/MOKP_p-6_n-30_1.dat"
)
```

Output is
```Julia
julia> using JuMP, HiGHS

julia> import MultiObjectiveAlgorithms as MOA

julia> function solve_tamby_mokp(filename)
lines = readlines(filename)
p = parse(Int, lines[1])
n = parse(Int, lines[2])
b = parse(Float64, lines[3])
C = collect(reduce(hcat, Float64.(x.args) for x in Meta.parse(lines[4]).args)')
w = Meta.parse(lines[5]).args
model = Model(() -> MOA.Optimizer(HiGHS.Optimizer))
set_attribute(model, MOA.Algorithm(), MOA.TambyVanderpooten())
@variable(model, x[1:n], Bin)
@constraint(model, w' * x <= b)
@objective(model, Max, C * x)
optimize!(model)
assert_is_solved_and_feasible(model)
return [value.(x; result) for result in 1:result_count(model)]
end
solve_tamby_mokp (generic function with 1 method)

julia> solve_tamby_mokp(
"/Users/odow/git/tambysatya/TamVan19/Instances/MOKP/MOKP_p-6_n-30_1.dat"
)
--------------------------------------------------------------------------------------------------
MultiObjectiveAlgorithms.jl
--------------------------------------------------------------------------------------------------
Algorithm: TambyVanderpooten
--------------------------------------------------------------------------------------------------
solve # Obj. 1 Obj. 2 Obj. 3 Obj. 4 Obj. 5 Obj. 6 Time
--------------------------------------------------------------------------------------------------
1 -1.17700e+03 -1.03200e+03 -1.07100e+03 -8.85000e+02 -8.46000e+02 -1.14100e+03 1.42563e+00
2 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.42763e+00
3 -1.07200e+03 -1.22100e+03 -1.18100e+03 -9.83000e+02 -8.90000e+02 -1.13000e+03 1.43889e+00
4 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.43945e+00
5 -8.84000e+02 -1.05600e+03 -1.37000e+03 -9.59000e+02 -8.78000e+02 -9.51000e+02 1.44168e+00
6 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.44223e+00
7 -8.95000e+02 -9.81000e+02 -1.09800e+03 -1.14300e+03 -6.92000e+02 -1.04800e+03 1.44480e+00
8 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.44535e+00
9 -9.14000e+02 -1.00700e+03 -1.11600e+03 -8.59000e+02 -1.11800e+03 -1.15500e+03 1.44794e+00
10 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.44849e+00
11 -9.23000e+02 -1.01000e+03 -9.91000e+02 -8.57000e+02 -7.84000e+02 -1.41500e+03 1.45187e+00
12 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 1.45241e+00
13 auxillary subproblem 1.49690e+00
14 -1.17700e+03 -1.03200e+03 -1.07100e+03 -8.85000e+02 -8.46000e+02 -1.14100e+03 1.69627e+00
15 auxillary subproblem 1.91702e+00
16 -9.14000e+02 -1.00700e+03 -1.11600e+03 -8.59000e+02 -1.11800e+03 -1.15500e+03 1.91964e+00
17 auxillary subproblem 1.92204e+00
18 -8.95000e+02 -9.81000e+02 -1.09800e+03 -1.14300e+03 -6.92000e+02 -1.04800e+03 1.92448e+00
19 auxillary subproblem 1.93331e+00
20 -1.07200e+03 -1.22100e+03 -1.18100e+03 -9.83000e+02 -8.90000e+02 -1.13000e+03 1.93606e+00
21 auxillary subproblem 1.93798e+00
22 -8.84000e+02 -1.05600e+03 -1.37000e+03 -9.59000e+02 -8.78000e+02 -9.51000e+02 1.94063e+00
23 auxillary subproblem 1.94374e+00
24 -9.23000e+02 -1.01000e+03 -9.91000e+02 -8.57000e+02 -7.84000e+02 -1.41500e+03 1.95118e+00
25 auxillary subproblem 1.96168e+00
26 -9.16000e+02 -1.00000e+03 -1.15100e+03 -1.13100e+03 -6.99000e+02 -1.02500e+03 1.97200e+00
27 auxillary subproblem 1.99172e+00
28 -8.72000e+02 -1.04100e+03 -1.13500e+03 -1.12900e+03 -7.53000e+02 -9.86000e+02 1.99935e+00
29 auxillary subproblem 2.01168e+00
30 -9.66000e+02 -9.78000e+02 -1.14400e+03 -1.12600e+03 -7.60000e+02 -1.01700e+03 2.02175e+00
31 auxillary subproblem 2.04473e+00
32 -8.70000e+02 -1.06700e+03 -1.20700e+03 -1.12200e+03 -7.63000e+02 -1.04500e+03 2.06123e+00
33 auxillary subproblem 2.08916e+00
34 -9.15000e+02 -1.06600e+03 -1.15200e+03 -1.12100e+03 -8.03000e+02 -1.14600e+03 2.09270e+00
35 auxillary subproblem 2.09913e+00
36 -8.71000e+02 -9.77000e+02 -1.21300e+03 -1.12100e+03 -8.38000e+02 -1.02200e+03 2.10892e+00
37 auxillary subproblem 2.11703e+00
38 -9.04000e+02 -1.09700e+03 -1.36600e+03 -9.91000e+02 -8.93000e+02 -1.09800e+03 2.11984e+00
39 auxillary subproblem 2.17713e+00
40 -9.15000e+02 -1.06600e+03 -1.15200e+03 -1.12100e+03 -8.03000e+02 -1.14600e+03 2.18210e+00
41 auxillary subproblem 2.18672e+00
42 -9.04000e+02 -1.00700e+03 -1.15700e+03 -8.87000e+02 -1.11300e+03 -1.08400e+03 2.18866e+00
43 auxillary subproblem 2.20184e+00
44 -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03 2.20434e+00
45 auxillary subproblem 2.21396e+00
46 -9.93000e+02 -1.03700e+03 -1.06000e+03 -8.98000e+02 -8.36000e+02 -1.41200e+03 2.22656e+00
47 auxillary subproblem 2.25049e+00
48 -9.38000e+02 -1.05300e+03 -1.18700e+03 -1.11400e+03 -8.50000e+02 -1.08400e+03 2.25784e+00
49 auxillary subproblem 2.30797e+00
50 -1.00700e+03 -1.11300e+03 -1.22100e+03 -1.10800e+03 -8.58000e+02 -1.15600e+03 2.31168e+00
... lines omitted ...
977 auxillary subproblem 1.39737e+02
978 -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03 1.39740e+02
979 auxillary subproblem 1.40425e+02
980 -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03 1.40427e+02
981 auxillary subproblem 1.41089e+02
982 -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03 1.41092e+02
^C--------------------------------------------------------------------------------------------------
TerminationStatus: INTERRUPTED
ResultCount: 276
--------------------------------------------------------------------------------------------------
ERROR: The model was not solved correctly. Here is the output of `solution_summary` to help debug why this happened:

solution_summary(; result = 1, verbose = false)
├ solver_name : MOA[algorithm=MultiObjectiveAlgorithms.TambyVanderpooten, optimizer=HiGHS]
├ Termination
│ ├ termination_status : INTERRUPTED
│ ├ result_count : 276
│ ├ raw_status : Solve complete. Found 276 solution(s)
│ └ objective_bound : [1.17700e+03,1.22100e+03,1.37000e+03,1.14300e+03,1.11800e+03,1.41500e+03]
├ Solution (result = 1)
│ ├ primal_status : FEASIBLE_POINT
│ ├ dual_status : NO_SOLUTION
│ └ objective_value : [1.17700e+03,1.03200e+03,1.07100e+03,8.85000e+02,8.46000e+02,1.14100e+03]
└ Work counters
└ solve_time (sec) : 1.41759e+02

Stacktrace:
[1] error(s::String)
@ Base ./error.jl:44
[2] #assert_is_solved_and_feasible#103
@ ~/.julia/packages/JuMP/e83v9/src/optimizer_interface.jl:1008 [inlined]
[3] assert_is_solved_and_feasible
@ ~/.julia/packages/JuMP/e83v9/src/optimizer_interface.jl:1002 [inlined]
[4] solve_tamby_mokp(filename::String)
@ Main ./REPL[4]:14
[5] top-level scope
@ REPL[5]:1
```

I don't know if this is expected behaviour of if we're not excluding some of the search boxes correctly.