pyswarms.discrete package¶
The pyswarms.discrete
module implements various techniques in
discrete optimization. These are techniques that can be applied to a
discrete search-space.
pyswarms.discrete.binary module¶
A Binary Particle Swarm Optimization (binary PSO) algorithm.
It takes a set of candidate solutions, and tries to find the best
solution using a position-velocity update method. Unlike
pyswarms.single.gb
and pyswarms.single.lb
, this technique
is often applied to discrete binary problems such as job-shop scheduling,
sequencing, and the like.
The update rule for the velocity is still similar, as shown in the proceeding equation:
For the velocity update rule, a particle compares its current position with respect to its neighbours. The nearest neighbours are being determined by a kD-tree given a distance metric, similar to local-best PSO. The neighbours are computed for every iteration. However, this whole behavior can be modified into a global-best PSO by changing the nearest neighbours equal to the number of particles in the swarm. In this case, all particles see each other, and thus a global best particle can be established.
In addition, one notable change for binary PSO is that the position update rule is now decided upon by the following case expression:
Where the function \(S(x)\) is the sigmoid function defined as:
This enables the algorithm to output binary positions rather than a stream of continuous values as seen in global-best or local-best PSO.
This algorithm was adapted from the standard Binary PSO work of J. Kennedy and R.C. Eberhart in Particle Swarm Optimization [SMC1997].
[SMC1997] | J. Kennedy and R.C. Eberhart, “A discrete binary version of particle swarm algorithm,” Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 1997. |
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class
pyswarms.discrete.binary.
BinaryPSO
(n_particles, dimensions, options, init_pos=None, velocity_clamp=None, vh_strategy='unmodified', ftol=-inf, ftol_iter=1)[source]¶ Bases:
pyswarms.base.base_discrete.DiscreteSwarmOptimizer
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__init__
(n_particles, dimensions, options, init_pos=None, velocity_clamp=None, vh_strategy='unmodified', ftol=-inf, ftol_iter=1)[source]¶ Initialize the swarm
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options
¶ a dictionary containing the parameters for the specific optimization technique
- c1 : float
- cognitive parameter
- c2 : float
- social parameter
- w : float
- inertia parameter
- k : int
- number of neighbors to be considered. Must be a
positive integer less than
n_particles
- p: int {1,2}
- the Minkowski p-norm to use. 1 is the sum-of-absolute values (or L1 distance) while 2 is the Euclidean (or L2) distance.
Type: dict with keys {'c1', 'c2', 'w', 'k', 'p'}
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init_pos
¶ option to explicitly set the particles’ initial positions. Set to
None
if you wish to generate the particles randomly.Type: numpy.ndarray, optional
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velocity_clamp
¶ a tuple of size 2 where the first entry is the minimum velocity and the second entry is the maximum velocity. It sets the limits for velocity clamping.
Type: tuple, optional
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vh_strategy
¶ a strategy for the handling of the velocity of out-of-bounds particles. Only the “unmodified” and the “adjust” strategies are allowed.
Type: String
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optimize
(objective_func, iters, n_processes=None, verbose=True, **kwargs)[source]¶ Optimize the swarm for a number of iterations
Performs the optimization to evaluate the objective function
f
for a number of iterationsiter.
Parameters: - objective_func (function) – objective function to be evaluated
- iters (int) – number of iterations
- n_processes (int, optional) – number of processes to use for parallel particle evaluation Defaut is None with no parallelization.
- verbose (bool) – enable or disable the logs and progress bar (default: True = enable logs)
- kwargs (dict) – arguments for objective function
Returns: the local best cost and the local best position among the swarm.
Return type:
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