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https://github.com/jmejia8/hardtestproblems.jl
Hard Test Problems for testing evolutionary algorithms.
https://github.com/jmejia8/hardtestproblems.jl
blackbox-optimization cec-2020 evolutionary-algorithms optimization pmm smd
Last synced: about 1 month ago
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Hard Test Problems for testing evolutionary algorithms.
- Host: GitHub
- URL: https://github.com/jmejia8/hardtestproblems.jl
- Owner: jmejia8
- License: mit
- Created: 2021-09-23T19:27:33.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-08T04:09:59.000Z (almost 3 years ago)
- Last Synced: 2024-11-02T15:14:58.096Z (about 2 months ago)
- Topics: blackbox-optimization, cec-2020, evolutionary-algorithms, optimization, pmm, smd
- Language: Julia
- Homepage:
- Size: 1.15 MB
- Stars: 4
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Hard Test Problems
This package implements challenging test problems for testing metaheuristics (evolutionary
algorithms) for single, multi-objective and bilevel optimization.
[](https://github.com/jmejia8/HardTestProblems.jl/actions)
[](https://codecov.io/gh/jmejia8/HardTestProblems.jl)
## Installation
Open the Julia (Julia 1.1 or Later) REPL and press `]` to open the Pkg prompt. To add this package, use the add command:
```
pkg> add HardTestProblems
```
Or, equivalently, via the `Pkg` API:
```julia
julia> import Pkg; Pkg.add("HardTestProblems")
```
## Benchmarks
- [x] [RW-MOP-2021](https://github.com/P-N-Suganthan/2021-RW-MOP)
Real world multi-objective Constrained Optimization Test Suite.
- [x] [CEC2020-BC-SO](https://github.com/P-N-Suganthan/2020-Bound-Constrained-Opt-Benchmark)
Bound-constrained test problems for single-objective optimization.
- [x] [PMM](https://doi.org/10.1016/j.amc.2021.126577) Pseudo-feasible solutions in evolutionary bilevel optimization: Test problems and performance assessment
- [x] [SMD](https://doi.org/10.1109/CEC.2012.6256557) Scalable test problems for single-objective bilevel optimization.
- [ ] [CEC2017](https://github.com/P-N-Suganthan/CEC2017) Competition on Constrained Real-Parameter Optimization.
## Usage
### RW-MOP-2021
Use the `get_RW_MOP_problem` to obtain the objective function and the corresponding
attributes:
```julia
julia> using HardTestProblems
julia> HardTestProblems.NAME_OF_PROBLEMS_RW_MOP_2021
50-element Vector{String}:
"pressure_vessel"
"vibrating_platform"
"two_bar_Truss_design_problems"
"weldan_beam_design"
⋮
"minimization_of_active_power_loss_in_islanded_microgrids_3"
"power_distribution_system_planning"
julia> f, conf = get_RW_MOP_problem(1);
julia> conf
Dict{Symbol, Any} with 8 entries:
:xmin => [0.51, 0.51, 10.0, 10.0]
:xmax => [99.49, 99.49, 200.0, 200.0]
:n => 4
:function => "pressure_vessel"
:gn => 2
:hn => 0
:fn => 2
:nadir => [3.59649e5, -7330.38]
julia> f(conf[:xmin])
([12.40080078125, -7330.382858376184], [0.0329, 0.1305], [0.0])
```
Note that `f` is in the form `f(x) = Tuple([f1, f2,...], [g1, g2,...], [h1, h2,...])`.
A feasible solution is such that `gi <= 0` and `hi = 0`.
### CEC2020-BC-SO
You can do the following to obtain the problem information:
```julia
julia> using HardTestProblems
julia> HardTestProblems.NAME_OF_PROBLEMS_CEC2020
10-element Vector{String}:
"cec2020_f1"
"cec2020_f2"
⋮
"cec2020_f10"
julia> f, conf = get_cec2020_problem(1, n=10);
julia> conf
Dict{Symbol, Any} with 8 entries:
:xmin => [-100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0]
:xmax => [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
:n => 10
:minimum => 100
:function => "cec2020_f1"
:gn => 0
:hn => 0
:fn => 1
julia> f(conf[:xmin])
2.0983079296144998e11
```
Each problems is defined for dimension `n in [2,5,10,15,20,30,50,100]`.
### PMM
```
julia> using HardTestProblems
julia> F, f, conf = PMM_get_problem(2,uldim=2,lldim=3);
julia> conf
Dict{Symbol, Any} with 15 entries:
:follower_optimum => 0.0
:n_inequality_follower => 0
:xbest => [0.0, 0.0]
:problem => "PMM2"
:n_equality_follower => 0
:lldim => 3
:uldim => 2
:n_equality_leader => 0
:n_inequality_leader => 0
:xmin => [-10.0, -10.0]
:leader_optimum => 0.0
:xmax => [10.0, 10.0]
:ymax => [10.0, 10.0, 10.0]
:ymin => [-10.0, -10.0, -10.0]
:ybest => [0.0, 1.41421, 1.73205]
```
### SMD
```julia
julia> using HardTestProblems
julia> F, f, conf = SMD_get_problem(12,uldim=2,lldim=3); # for SMD12
julia> conf
Dict{Symbol, Any} with 14 entries:
:follower_optimum => 4.0
:n_inequality_follower => 3
:xbest => [1.0, 1.0]
:n_equality_follower => 0
:lldim => 3
:uldim => 2
:n_equality_leader => 0
:n_inequality_leader => 3
:xmin => [-5.0, -1.0]
:leader_optimum => 3.0
:xmax => [10.0, 1.0]
:ymax => [10.0, 10.0, 0.785388]
:ymin => [-5.0, -5.0, -0.785388]
:ybest => [1.0, 1.0, 0.0]
julia> F(conf[:xbest], conf[:ybest]) # upper level function
(3.0, [-0.0, -0.0, -1.0], [0.0])
julia> f(conf[:xbest], conf[:ybest]) # lower level function
(4.0, [-0.0, -0.0, -0.0], [0.0])
```