Balans (IJCAI'25) is an online-learning meta-solver designed to tackle Mixed-Integer Programming problems (MIPs) through multi-armed bandit-based adaptive large neighborhood search strategy, ALNS(MIP).
The framework integrates several powerful components into a highly configurable, modular, extensible, solver-agnostic, open-source software:
- MABWiser for contextual multi-armed bandits
- ALNS for adaptive large neighborhood search
- SCIP and Gurobi for solving mixed-integer linear programming problems.
ParBalans (Arxiv'25) extends this framework with parallelization strategies at both the outer configuration level and the inner branch-and-bound level to exploit modern multicore architectures.
More broadly, Balans is an integration technology at the intersection of adaptive search, meta-heuristics, multi-armed bandits, and mixed integer programming. When configured with a single neighborhood, it generalizes and subsumes prior work on Large Neighborhood Search for MIP, LNS(MIP). A detailed description of the framework, algorithms, and experimental results can be found in our IJCAI'25 presentation.
Balans is a collaborative effort between academia and industry, developed by Brown University, the University of Southern California, and the AI Center of Excellence at Fidelity Investments. The project is contributed to the COIN-OR Foundation.
# ALNS for adaptive large neigborhood search
from alns.select import MABSelector
from alns.accept import HillClimbing, SimulatedAnnealing
from alns.stop import MaxIterations, MaxRuntime
# MABWiser for contextual multi-armed bandits
from mabwiser.mab import LearningPolicy
# Balans meta-solver for solving mixed integer programming problems
from balans.solver import Balans, DestroyOperators, RepairOperators
# Destroy operators
destroy_ops = [DestroyOperators.Crossover,
DestroyOperators.Dins,
DestroyOperators.Mutation_25,
DestroyOperators.Local_Branching_10,
DestroyOperators.Rins_25,
DestroyOperators.Proximity_05,
DestroyOperators.Rens_25,
DestroyOperators.Random_Objective]
# Repair operators
repair_ops = [RepairOperators.Repair]
# Rewards for online learning feedback loop
best, better, accept, reject = 4, 3, 2, 1
# Bandit selector
selector = MABSelector(scores=[best, better, accept, reject],
num_destroy=len(destroy_ops),
num_repair=len(repair_ops),
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.50))
# Acceptance criterion
# accept = HillClimbing() # for pure exploitation
accept = SimulatedAnnealing(start_temperature=20, end_temperature=1, step=0.1)
# Stopping condition
# stop = MaxRuntime(100)
stop = MaxIterations(10)
# Balans
balans = Balans(destroy_ops=destroy_ops,
repair_ops=repair_ops,
selector=selector,
accept=accept,
stop=stop,
mip_solver="scip") # "gurobi"
# Run a mip instance to retrieve results
instance_path = "data/miplib/noswot.mps"
result = balans.solve(instance_path)
# Results of the best found solution and the objective
print("Best solution:", result.best_state.solution())
print("Best solution objective:", result.best_state.objective())# Parallel version of Balans, that runs several configurations parallely
from balans.solver import ParBalans
from alns.stop import MaxIterations
# ParBalans to run different Balans configs in parallel and save results
parbalans = ParBalans(n_jobs=2, # Outer-level: parallel Balans configurations
n_mip_jobs=1, # Inner-level: parallel BnB search. Only supported by Gurobi solver
mip_solver="scip",
output_dir="results/")
# Run a mip instance to retrieve several results
instance_path = "data/miplib/noswot.mps"
best_solution, best_objective = parbalans.run(instance_path)
# Results of the best found solution and the objective
print("Best solution:", best_solution)
print("Best solution objective:", best_objective)- Dins1
- Local Branching2
- Mutation3
- Rens4
- Rins5
- Random Objective6
- Proximity Search7
- Crossover8
- Repair MIP
Balans requires Python 3.10+ can be installed from PyPI via pip install balans. More details in INSTALL.
If you use Balans in a publication, please cite it as:
@inproceedings{balans,
title = {Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problems},
author = {Cai, Junyang and Kadıoğlu, Serdar and Dilkina, Bistra},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {2566--2574},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/286},
url = {https://doi.org/10.24963/ijcai.2025/286},
}
@misc{parbalans,
title={ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search},
author={Alican Yilmaz and Junyang Cai and Serdar Kadıoğlu and Bistra Dilkina},
year={2025},
eprint={2508.06736},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.06736},
}Balans is licensed under the Apache License 2.0.
Footnotes
-
S. Ghosh. DINS, a MIP Improvement Heuristic. Integer Programming and Combinatorial Optimization: IPCO, 2007. ↩
-
M. Fischetti and A. Lodi. Local branching. Mathematical Programming, 2003. ↩
-
Rothberg. An Evolutionary Algorithm for Polishing Mixed Integer Programming Solutions. INFORMS Journal on Computing, 2007. ↩
-
Berthold. RENS–the optimal rounding. Mathematical Programming Computation, 2014. ↩
-
E. Danna, E. Rothberg, and C. L. Pape. Exploring relaxation induced neighborhoods to improve MIP solutions. Mathematical Programming, 2005. ↩
-
Random Objective. ↩
-
M. Fischetti and M. Monaci. Proximity search for 0-1 mixed-integer convex programming. Journal of Heuristics, 20(6):709–731, Dec 2014. ↩
-
E. Rothberg. An Evolutionary Algorithm for Polishing Mixed Integer Programming Solutions. INFORMS Journal on Computing, 19(4):534–541, 2007. ↩