https://github.com/jmejia8/bilevelheuristics.jl
Metaheuristics for solving bilevel optimization problems.
https://github.com/jmejia8/bilevelheuristics.jl
bilevel-optimization evolutionary-algorithms metaheuristics
Last synced: 3 months ago
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Metaheuristics for solving bilevel optimization problems.
- Host: GitHub
- URL: https://github.com/jmejia8/bilevelheuristics.jl
- Owner: jmejia8
- License: mit
- Created: 2022-02-03T21:58:05.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-05T01:43:35.000Z (about 1 year ago)
- Last Synced: 2025-02-20T19:47:06.629Z (3 months ago)
- Topics: bilevel-optimization, evolutionary-algorithms, metaheuristics
- Language: Julia
- Homepage: https://jmejia8.github.io/BilevelHeuristics.jl/dev/
- Size: 280 KB
- Stars: 12
- Watchers: 4
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# BilevelHeuristics.jl
This package implements different heuristic and metaheuristic algorithms for
Bilevel Optimization (BO).
[](https://app.travis-ci.com/jmejia8/BilevelHeuristics.jl)
**BilevelHeuristics.jl is still in early development so please send feedback or open issues.**
## Installation
Open the Julia REPL (Julia 1.6 or later) and press `]` to open the Pkg prompt. To add this
package, use the `add` command:
Type `]`
```julia-repl
pkg> add BilevelHeuristics
```
Or, equivalently, via the `Pkg` API:
```julia
julia> import Pkg; Pkg.add("BilevelHeuristics")
```
## Algorithms
Current implemented Bilevel Metaheuristics include:
- [x] [BCA](https://doi.org/10.1109/ROPEC.2018.8661368): Bilevel Centers Algorithm
- [x] [QBCA](https://doi.org/10.1109/CEC.2019.8790097): BCA with a lower-level Quasi-Newton optimization method.
- [x] [QBCA2](https://doi.org/10.1016/j.amc.2021.126577): Improved QBCA (implements conditions to avoid pseudo-feasible solutions)
- [x] [SABO](https://doi.org/10.1145/3377930.3390236): Surrogate-assisted Bilevel Optimization.
- [x] SMS-MOBO: S-metic-selection-based Multi-objective Bilevel Optimization.
- [x] BLEMO: Bilevel Evolutionary Multi-objective Optimization
## Example
The following example illustrates the usage of `BilevelHeuristics.jl`. Here, `BCA` is used,
but this example works for the other optimizers.
Defining objective functions corresponding to the BO problem.
**Upper level (leader problem):**
```julia
using BilevelHeuristics
F(x, y) = sum(x.^2) + sum(y.^2)
bounds_ul = [-ones(5) ones(5)]
```
**Lower level (follower problem):**
```julia
f(x, y) = sum((x - y).^2) + y[1]^2
bounds_ll = [-ones(5) ones(5)];
```
**Approximate solution:**
```julia
res = optimize(F, f, bounds_ul, bounds_ll, BCA())
```
**Output:**
```
+=========== RESULT ==========+
iteration: 108
minimum:
F: 4.03387e-10
f: 2.94824e-10
minimizer:
x: [-1.1460768817533927e-5, 7.231706879604178e-6, 3.818596951258517e-6, 2.294324313691869e-6, 1.8770952450067828e-6]
y: [1.998748659975197e-6, 9.479307908087866e-6, 6.180041276047425e-6, -7.642051857319683e-6, 2.434166021682429e-6]
F calls: 2503
f calls: 5062617
Message: Stopped due UL function evaluations limitations.
total time: 26.8142 s
+============================+
```
```julia
x, y = minimizer(res) # upper (x) and lower (y) level decision vectors
Fmin, fmin = minimum(res) # upper and lower objective values.
```
## TO DO
- [ ] Parallelize the lower-level optimization procedures.