A flexible implementation of the adaptive large neighbourhood search (ALNS) algorithm.
This package offers a general, well-documented and tested implementation of the adaptive large neighbourhood search (ALNS) meta-heuristic, based on the description given in Pisinger and Ropke (2010). It may be installed in the usual way as,
pip install alns
How to use
alns package exposes two classes,
State. The first
may be used to run the ALNS algorithm, the second may be subclassed to
store a solution state - all it requires is to define an
The ALNS algorithm must be supplied with an acceptance criterion, to
determine the acceptance of a new solution state at each iteration.
An overview of common acceptance criteria is given in Santini et al.
(2018). Several have already been implemented for you, in
HillClimbing. The simplest acceptance criterion, hill-climbing solely accepts solutions improving the objective value.
RecordToRecordTravel. This criterion only accepts solutions when the improvement meets some updating threshold.
SimulatedAnnealing. This criterion accepts solutions when the scaled probability is bigger than some random number, using an updating temperature.
Each acceptance criterion inherits from
AcceptanceCriterion, which may
be used to write your own.
examples/ directory features some example notebooks showcasing
how the ALNS library may be used. Of particular interest are,
- The travelling salesman problem (TSP), here. We solve an instance of 131 cities to within 2.1% of optimality, using simple destroy and repair heuristics with a post-processing step.
- The cutting-stock problem (CSP), here. We solve an instance with 180 beams over 165 distinct sizes to within 1.35% of optimality in only a very limited number of iterations.
- Pisinger, D., and Ropke, S. (2010). Large Neighborhood Search. In M. Gendreau (Ed.), Handbook of Metaheuristics (2 ed., pp. 399-420). Springer.
- Santini, A., Ropke, S. & Hvattum, L.M. (2018). A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic. Journal of Heuristics 24 (5): 783-815.
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