`particle_swarm_optimization(Problem,RandomAlgorithm)`

Problem - Problem object implementing particle_swarm_optimization_protocol.
RandomAlgorithm - Random number generator algorithm for the fast_random library. Continuous bounded global-best particle swarm optimization algorithm. Parameterized by a problem object implementing the particle_swarm_optimization_protocol protocol and by a random number generator algorithm for the fast_random library. The algorithm minimizes or maximizes the fitness function defined by the problem.
| Availability: | logtalk_load(particle_swarm_optimization(loader))

| Author: Paulo Moura | Version: 1:0:0 | Date: 2026-08-19

| Compilation flags: | static, context_switching_calls

| Imports: | public :ref:`options <options/0>` | Uses: | :ref:`fast_random(Algorithm) <fast_random/1>` | :ref:`linear_algebra <linear_algebra/0>` | :ref:`numberlist <numberlist/0>` | :ref:`type <type/0>`

| Remarks:
- Algorithm: Uses synchronous global-best particle swarm optimization. Every particle update in an iteration uses the global best from the start of that iteration.
- Optimization objective: The objective(minimize|maximize) option selects the fitness ordering. Fitness values are otherwise used unchanged.
- Target fitness: The target_fitness(Fitness) option stops the run when the best fitness reaches or passes the target in the selected objective direction.
- Stagnation stopping: The stagnation_iterations(N) option stops the run after N consecutive iterations without a strict global-best improvement; zero disables this condition.
- Initial velocities: If the problem defines initial_velocities/1, its velocities are validated and used. Otherwise, velocities are sampled randomly.
- Boundary handling: Velocities are limited to plus or minus the range of each dimension. A position crossing a bound is clamped to that bound and its velocity component is set to zero.
- Progress reporting: If the problem object defines progress/5, it is called periodically and once when the loop terminates.
- Seed control: The seed(S) option initializes the random number generator for reproducible runs.
| Inherited public predicates: | Â :ref:`options_protocol/0::check_option/1` Â :ref:`options_protocol/0::check_options/1` Â :ref:`options_protocol/0::default_option/1` Â :ref:`options_protocol/0::default_options/1` Â :ref:`options_protocol/0::option/2` Â :ref:`options_protocol/0::option/3` Â :ref:`options_protocol/0::valid_option/1` Â :ref:`options_protocol/0::valid_options/1` Â

.. contents:: :local: :backlinks: top

Public predicates

.. index:: run/2 .. _particle_swarm_optimization/2::run/2:

run/2 ^^^^^^^^^

Runs the particle swarm optimization algorithm using default options and returns the best position and fitness found.

| Compilation flags: | static

| Template: | run(BestPosition,BestFitness) | Mode and number of proofs: | run(-list(number),-number) - one

.. index:: run/3 .. _particle_swarm_optimization/2::run/3:

run/3 ^^^^^^^^^

Runs the particle swarm optimization algorithm using the given options and returns the best position and fitness found.

| Compilation flags: | static

| Template: | run(BestPosition,BestFitness,Options) | Mode and number of proofs: | run(-list(number),-number,+list(compound)) - one

| Remarks:

objective(Objective) option: Optimization objective, either minimize or maximize (default: minimize).
target_fitness(Fitness) option: Numeric target that stops the run when reached or passed in the selected objective direction (default: none).
max_iterations(N) option: Maximum number of swarm iterations (default: 1000).
stagnation_iterations(N) option: Number of consecutive iterations without a strict global-best improvement before stopping; zero disables this condition (default: 0).
inertia_weight(W) option: Velocity inertia weight (default: 0.7298).
cognitive_coefficient(C) option: Personal-best acceleration coefficient (default: 1.49618).
social_coefficient(C) option: Global-best acceleration coefficient (default: 1.49618).
updates(N) option: Number of progress reports during the run; zero disables reporting (default: 0).
seed(S) option: Positive integer random seed for reproducible runs.

.. index:: run/4 .. _particle_swarm_optimization/2::run/4:

run/4 ^^^^^^^^^

Runs the particle swarm optimization algorithm using the given options and returns the best position, best fitness, and run statistics.

| Compilation flags: | static

| Template: | run(BestPosition,BestFitness,Statistics,Options) | Mode and number of proofs: | run(-list(number),-number,-list(compound),+list(compound)) - one

| Remarks:

Statistics list: A list containing iterations(N), evaluations(E), improvements(I), final_mean_fitness(M), and final_diversity(D). Improvements are measured in the selected objective direction.

Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

.. seealso::

:ref:`particle_swarm_optimization(Problem) <particle_swarm_optimization/1>`, :ref:`particle_swarm_optimization_protocol <particle_swarm_optimization_protocol/0>`