saealib.PSO#

class saealib.PSO(w=0.7, c1=1.5, c2=1.5, v_max=None)[source]#

Bases: AskTellAlgorithm

Particle Swarm Optimization (PSO) for single-objective problems.

The global best (leader) is selected from the personal bests of all particles using ctx.comparator, so the ranking adapts automatically to any single-objective Comparator.

Multi-objective PSO (MOPSO) requires a dedicated subclass with a separate Pareto archive for leader selection.

Parameters:

w (float)
c1 (float)
c2 (float)
v_max (float | None)

w#

Inertia weight.

Type:: float

c1#

Cognitive coefficient (personal best attraction).

Type:: float

c2#

Social coefficient (global best attraction).

Type:: float

v_max#

Maximum velocity magnitude per dimension. None disables clamping.

Type:: float or None

References

[38]: Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN), 4, 1942-1948.

[39]: Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC), 69-73. (Introduces the inertia weight w used here.)

Methods

`__init__`	Initialize PSO.
`ask`	Update particle velocities and positions.
`contract`	Return the PSO contract with comparator-backed feedback.
`create_pareto_archive`	Create a ParetoArchive with the correct direction for the problem.
`get_required_attrs`	Return PSO-specific attributes required by the Population.
`tell`	Update the population and personal bests from evaluated offspring.

Method Details

PSO.__init__(w=0.7, c1=1.5, c2=1.5, v_max=None)[source]#

Initialize PSO.

Parameters:

w (float, optional) – Inertia weight. Defaults to 0.7.
c1 (float, optional) – Cognitive coefficient. Defaults to 1.5.
c2 (float, optional) – Social coefficient. Defaults to 1.5.
v_max (float or None, optional) – Maximum velocity per dimension. Defaults to None (no clamping).

PSO.ask(request, state)[source]#

Update particle velocities and positions.

Parameters:

request (ProposalRequest) – Request-specific offspring count; PSO uses its population size.
state (StateView) – Read-only algorithm state view.

Returns:

Candidates with updated x and velocity.

Return type:

ProposalBatch

PSO.contract()[source]#

Return the PSO contract with comparator-backed feedback.

Return type:: ComponentContract

PSO.create_pareto_archive(attrs, init_capacity, problem)#

Create a ParetoArchive with the correct direction for the problem.

Parameters:

attrs (list[PopulationAttribute])
init_capacity (int)
problem (Problem)

Return type:

ParetoArchive

PSO.get_required_attrs(problem)[source]#

Return PSO-specific attributes required by the Population.

Parameters:: problem (Problem) – The Problem object being referenced.
Returns:: velocity, pbest_x, pbest_f, and pbest_cv attributes.
Return type:: list[PopulationAttribute]

PSO.tell(feedback, state)[source]#

Update the population and personal bests from evaluated offspring.

Parameters:

feedback (FeedbackBatch) – Feedback delivered for the current proposal.
state (StateView) – Read-only algorithm state view.

Return type:

StatePatch