culebra.solution.tsp.Ant class

class Ant(species: Species, fitness_cls: Type[Fitness], path: Sequence[int] | None = None)

Construct a default solution.

Parameters:

  • species (species_cls) – The species the solution will belong to

  • fitness (Fitness) – The solution’s fitness class

  • path (Sequence of int) – Initial path

Raises:

  • TypeError – If species is not a valid species

  • TypeError – If fitness_cls is not a valid fitness class

Class attributes

Ant.species_cls = <class 'culebra.solution.tsp.Species'>

Class for the species used by the Solution class to constrain all its instances.

Class methods

classmethod Ant.load_pickle(filename: str) → Base

Load a pickled object from a file.

Parameters:

filename (str) – The file name.

Raises:

Properties

property Ant.species: Species

Get the solution’s species.

Returns:

The species

Return type:

Species

property Ant.fitness: Fitness

Get and set the solution’s fitness.

Getter:

Return the current fitness

Setter:

Set a new Fitness

Type:

Fitness

property Ant.path: Sequence[int]

Get and set the path.

Getter:

Return the path.

Setter:

Set the new path. An array-like object of node indices is expected

Type:

Sequence of int

Raises:

ValueError – If the new path does not meet the species constraints.

property Ant.current: int

Return the current node in the path.

Type:

int

property Ant.discarded: Sequence[int]

Nodes discarded by the ant.

Return an empty sequence since all nodes must be visited for the TSP problem.

Type:

Sequence

Methods

Ant.save_pickle(filename: str) → None

Pickle this object and save it to a file.

Parameters:

filename (str) – The file name.

Raises:
Ant.append(node: int) → None

Append a new node to the ant’s path.

Raises:

  • TypeError – If node is not an integer number

  • ValueError – If node does not meet the species constraints.

  • ValueError – If node is already in the path.

Ant.discard(node: int) → None

Discard a node.

This method raises an exception since nodes can not be discarded for the TSP problem.

Parameters:

node (int) – The node

Raises:

RuntimeError – If called

Ant.dominates(other: Solution) → bool

Dominate operator.

Parameters:

other (Solution) – Other solution

Returns:

True if each objective of the solution is not strictly worse than the corresponding objective of other and at least one objective is strictly better.

Return type:

bool

Ant.delete_fitness() → None

Delete the solution’s fitness.

Private methods

Ant._setup() → None

Set the default path for ants.

An empty path is set.

Dunder methods

Ant.__hash__() → int

Return the hash number for this solution.

The hash number is used for equality comparisons. Currently is implemented as the hash of the solution’s string representation.

Ant.__eq__(other: Solution) → bool

Equality test.

Parameters:

other (Solution) – Other solution

Returns:

True if other codes the same solution, or False otherwise

Return type:

bool

Ant.__ne__(other: Solution) → bool

Not equality test.

Parameters:

other (Solution) – Other solution

Returns:

False if other codes the same solutions, or True otherwise

Return type:

bool

Ant.__lt__(other: Solution) → bool

Less than operator.

Parameters:

other (Solution) – Other solution

Returns:

True if the solution’s fitness is less than the other’s fitness

Return type:

bool

Ant.__gt__(other: Solution) → bool

Greater than operator.

Parameters:

other (Solution) – Other solution

Returns:

True if the solution’s fitness is greater than the other’s fitness

Return type:

bool

Ant.__le__(other: Solution) → bool

Less than or equal to operator.

Parameters:

other (Solution) – Other solution

Returns:

True if the solution’s fitness is less than or equal to the other’s fitness

Return type:

bool

Ant.__ge__(other: Solution) → bool

Greater than or equal to operator.

Parameters:

other (Solution) – Other solution

Returns:

True if the solution’s fitness is greater than or equal to the other’s fitness

Return type:

bool

Ant.__str__() → str

Return the solution as a string.

A symmetric tsp problem is assumed. Thus, all rotations of paths [0, 1, …, n] and [n, …, 1, 0] are considered the same path.

The path is rolled to start with the node with smallest index.