.. index:: single: particle_swarm_optimization(Problem,RandomAlgorithm)
.. _particle_swarm_optimization/2:

.. rst-class:: right

**object**

``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:**
|     :ref:`options_protocol/0::check_option/1`  :ref:`options_protocol/0::check_options/1`  :ref:`options_protocol/0::default_option/1`  :ref:`options_protocol/0::default_options/1`  :ref:`options_protocol/0::option/2`  :ref:`options_protocol/0::option/3`  :ref:`options_protocol/0::valid_option/1`  :ref:`options_protocol/0::valid_options/1`  

.. 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>`